Drought Index Research Articles

Study of Drought Using the Theory of Run and Hydrological Drought Index and its Relationship to Reservoir Storage at the Batubai Dam and Pengga Dam

Drought is a frequent disaster in Indonesia, particularly in areas with long dry seasons. Several factors influencing drought in Indonesia include irregular rainfall patterns, climate change, and unsustainable water resource usage. This study was conducted to provide an overview of drought and its impact on the storage of the Batujai Dam and Pengga Dam. This research utilizes drought analysis and reservoir simulation analysis. The results of this study are as follows: a) The drought condition at the Batujai Dam based on the Theory of Run method has a maximum deficit value of -644.66 mm, and the drought based on the Hydrological Drought Index using inflow data from 2013 to 2022 has a maximum deficit value of -2.88 m³/s. Meanwhile, the drought condition at the Pengga Dam based on the Theory of Run method has a maximum deficit value of -407.86 mm, and the drought based on the Hydrological Drought Index using inflow data has a maximum deficit value of -2.371 m³/s. b) The impact of drought on the simulation of the Batujai Dam and Pengga Dam over specific periods shows that the Batujai Dam was full (overflowing) for 7 dekadal periods, whereas under existing conditions, it was never full. The Pengga Dam was full (overflowing) for 6 dekadal periods, while under existing conditions, it was only full once, with a K Factor (ratio of water supply to water demand) having a minimum value of 20% from October I to October III, 2018.

Agricultural Drought-Triggering for Anticipatory Action in Papua New Guinea

Throughout its history, Papua New Guinea (PNG) has faced recurrent agricultural droughts, imposing considerable strain on both livelihoods and the economy. Particularly severe droughts have been associated with El Niño climate patterns. During these episodes, PNG becomes especially vulnerable to extended periods of aridity and diminished precipitation. Historically, humanitarian assistance for these events has primarily focused on responding to emergencies after an agricultural drought has been declared and communities have already been impacted. Here, we developed a proactive agricultural drought-triggering method for anticipatory action (AA) in PNG to offer a more sustainable and cost-effective approach to address this hazard. Our AA uses weather forecasts and risk data to identify and implement mitigative actions before a disaster occurs. The research details a step-by-step guide from early warning to action implemented by the Food and Agricultural Organization of the United Nations and the Government of Papua New Guinea. This preemptive disaster risk management initiative integrates a combined drought index (CDI) with specific thresholds and tailored anticipatory actions based on crop calendars. Moreover, the developed CDI provides a 3-month lead time for implementing AA to reduce the impact of the agricultural drought. During the El Niño-induced drought event that began in 2023, the CDI was tested and the AA was piloted for the first time.

Effects of arbuscular mycorrhizal fungi in the rhizosphere of two olive (Olea europaea) varieties Arbequina and Barnea under water deficit conditions.

One strategy to improve olive (Olea europaea ) tree drought tolerance is through the symbiosis of arbuscular mycorrhizal fungi (AMF), which helps alleviate water deficit through a combination of morphophysiological effects. Cuttings of olive varieties Arbequina (A) and Barnea (B) were grown with (+AMF) or without (-AMF) inoculum in the olive grove rhizosphere soil. One year after establishment, pots were exposed to four different water regimes: (1) control (100% of crop evapotranspiration); (2) short-period drought (20days); (3) long-period drought (25days); and (4) rewatering (R). To evaluate the influence of AMF on tolerance to water stress, stem water potential, stomatal conductance and the biomarkers for water deficit malondialdehyde, proline, soluble sugars, phenols, and flavonoids were evaluated at the end of the irrigation regimes. Stem water potential showed higher values in A(+) and B(+) in all water conditions, and the opposite was true for stomatal conductance. For proline and soluble sugars, the stem water potential trend is repeated with some exceptions. AMF inoculum spore communities from A(+ and -) and B(+ and -) were characterised at the morphospecies level in terms of richness and abundance. Certain morphospecies were identified as potential drought indicators. These results highlight that the benefits of symbiotic relationships between olive and native AMF can help to mitigate the effects of abiotic stress in soils affected by drought.

Concurrent drought threatens wheat and maize production and will widen crop yield gaps in the future

CONTEXTDrought poses a significant threat to global crop production. As the global community grapples with the escalating challenges of climate change, understanding the multifaceted impacts of concurrent drought on food security becomes imperative. OBJECTIVEThis study delved into the response of wheat and maize, key staples in the global food system, to different types of drought, with a particular focus on the yield gaps resulting from concurrent meteorological and agricultural drought. METHODSThe DSSAT-CERES model was adopted to simulate phenophase, rain-fed, and potential yields of maize and wheat in China from 1962 to 2100. Meteorological (Non-stationary Standard Precipitation Evapotranspiration Index, NSPEI) and agricultural (Standard Soil Moisture Index, SSMI) drought indices were calculated from crop seeding to maturity stages. We employed bivariate and multiple cross-wavelet as well as vine Copula to qualitatively and quantitatively analyze the response of yield gaps to different drought types. Finally, we determined the relative dependence weights of maize and wheat on NSPEI and SSMI by least squares regression. RESULTS AND CONCLUSIONSSpanning from 2022 to 2100, a trend of shortened growth periods for these crops were detected, accompanied by increasingly drier conditions. These situations exacerbated the crops' vulnerability to concurrent drought, leading to considerable yield reductions. Our projections indicated that future yield gaps due to concurrent drought could be, on average, 2–30% higher than those caused by single-type drought. Concurrent drought affected wheat (5–50%) more severely than maize (0–35%). Western regions would be more affected than the Eastern regions. Under the SSP (Shared socioeconomic pathway) 5-8.5 scenario in 2022–2100, all four crops would have higher dependence weights on SSMI (51–99%) than NSPEI (26–59%), emphasizing the critical role of soil moisture in agricultural drought monitoring and yield loss alleviation. SIGNIFICANCEOur findings highlight the urgent need for integrated drought management strategies that address the compounded risks of concurrent drought, thereby contributing to the resilience of agricultural systems and global food security in a changing climate. Our research proposes to consider the relative weights of meteorological and agricultural drought in the future development of composite drought monitoring indicators for addressing food drought risk under climate change.

The enhancive effect of the 2014-2016 El Niño-induced drought on the control of soil-transmitted helminthiases without anthelmintics: A longitudinal study.

Soil-transmitted helminthiases (STHs) are common in tropical and subtropical regions. Southern Thailand experiences an extended rainy season, leading to persistently moist soil. This condition supports the life cycle of STHs, hindering effective control due to reinfection and low drug efficacy. We implemented a novel STH control strategy during the dry season aimed at decreasing reinfection rates without enhancing sanitation or hygiene practices. However, there were unexpected, prolonged droughts linked to El Niño events from 2014 to 2016. Additionally, we assessed the effects of these drought conditions on further control measures without the use of anthelmintics. A longitudinal study was conducted from 2012 to 2016. Stool samples collected from 299 participants were analyzed using the Kato-Katz and agar plate culture methods. Participants who tested positive for STHs received a single 400 mg dose of albendazole. The efficacy of the treatment was evaluated three weeks later. To confirm the control measures were implemented during the dry season, we monitored the number of rainy days following albendazole treatment for 52 days, of which 38 were without rain. Follow-up stool examinations were carried out in 2013 and 2016, with no additional doses of albendazole administered. Rainfall and rainy day data, which served as indicators of unexpected droughts due to El Niño, were collected from the nearest local meteorological stations. Before the drought, there was a decrease in STH prevalence in 2013-except for trichuriasis-attributable to the dry season control efforts. Despite these efforts, STH prevalence remained high. Remarkably, in 2016, following the drought period, the prevalence of trichuriasis, which had not changed previously, spontaneously declined without further albendazole treatment compared to 2013. Furthermore, the prevalence of strongyloidiasis remained unchanged likely due to its low susceptibility to drought conditions, as it can reproduce within hosts. Conversely, the prevalence of other STHs consistently declined. The drought and possible improvements in sanitation and hygiene practices contributed to this decrease by reducing rates of reinfection and new infection and by increasing the natural cure rate. Additionally, some participants infected with hookworms or Trichuris who were not cured by albendazole experienced natural remission. Control measures implemented during the dry season, combined with a 14-month-long drought induced by the El Niño event of 2014-2016, and some improvements in sanitation and hygiene practices, contributed to a decrease in both the prevalence and intensity of STHs, except for S. stercoralis. Over time, S. stercoralis is likely to become the predominant species among the STHs.

Auto-Machine-Learning Models for Standardized Precipitation Index Prediction in North–Central Mexico

Certain impacts of climate change could potentially be linked to alterations in rainfall patterns, including shifts in rainfall intensity or drought occurrences. Hence, predicting droughts can provide valuable assistance in mitigating the detrimental consequences associated with water scarcity, particularly in agricultural areas or densely populated urban regions. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applied an Auto-Machine-Learning approach to deploy Artificial Neural Network models, aiming to predict the Standardized Precipitation Index in four regions of Zacatecas, Mexico. Climatological time-series data spanning from 1979 to 2020 were utilized as predictive variables. The best models were found using performance metrics that yielded a Mean Squared Error, Mean Absolute Error, and Coefficient of Determination ranging from 0.0296 to 0.0388, 0.1214 to 0.1355, and 0.9342 to 0.9584, respectively, for the regions under study. As a result, the Auto-Machine-Learning approach successfully developed and tested Artificial Neural Network models that exhibited notable predictive capabilities when estimating the monthly Standardized Precipitation Index within the study region.

Evaluation of the performance of meteorological drought indices in Morocco: A case study of different climatic zones

AbstractUnderstanding drought occurrence and evolution is important in minimizing the impacts associated with it. This work assesses the performance of 10 commonly used meteorological indices to measure drought in Morocco. The studied indices are Deciles Index (DI), Percent of Normal Index (PNI), Z‐Score Index (ZSI), China‐Z Index (CZI), Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), its self‐calibrated variant (scPDSI) and Palmer Z Index (PZI). Rainfall and temperature gridded data is sourced from PERSIANN‐CDR and ERA5, respectively, for the period 1983–2021. The study area exhibits three main climatic regimes; subhumid, semi‐arid and arid, with a drying and warming climate, as depicted by the rainfall and temperature trends analysis. Results show that most rainfall‐based indices perform relatively poorly in drought monitoring in the study area. DI and PNI appear to be inconsistent and abnormally responsive to rainfall. RAI reports droughts 56.5% more frequently and slightly underestimate drought intensity compared to other indices. Similarly, ZSI and CZI largely underestimate drought intensity. PDSI and scPDSI are computationally demanding, often underestimate drought intensity and overestimate drought duration by at least 115% compared to SPI and SPEI. Conversely, PZI can be used for drought onset detection as it reported droughts early compared to the other indices. SPI and SPEI perform overall better regarding their consistent drought identification and severity assessment. However, SPEI is found to be more suitable than SPI in the arid and semi‐arid regions and performed better considering the warming climate of the country.

A novel vegetation-water resistant soil moisture index for remotely assessing soil surface moisture content under the low-moderate wheat cover

Soil surface moisture content (SMC) is one of the most critical and fundamental indicators of crop drought because water is vital for vegetation growth. In recent decades, satellite high-spatial-resolution optical multi-spectral remote-sensing sensors have entered a new stage. However, current optical remote sensing SMC indices (SMIs) are sensitive to cropland SMC and vegetation water content (VWC). From dry to saturated, the soil spectra decreased quickly as the SMC increased. For the vegetation spectra, the short-wave infrared (SWIR) bands decreased as the VWC increased. The differences in the responses of near-infrared (NIR) and SWIR bands to water may help distinguish soil moisture from mixed soil-vegetation spectra. This study focuses on (a) analyzing how soil and vegetation reflectance are affected by VWC and SMC, (b) designing a new vegetation-water resistant soil moisture index (VRSMI) based on the differences in the responses of NIR bands and SWIR bands to water, and (c) evaluating the potential of using VRSMI to remotely estimate SMC in low-moderate crop-covered regions. VRSMI uses SWIR1-SWIR2 as the numerator to detect the water absorption features and NIR×(SWIR1)0.5 as the denominator to weaken the vegetation effect. VRSMI and soil-VRSMI (VRSMIs) were tested using laboratory- and field-based spectra datasets. Our study presents the following conclusions: (1) The differences in the responses of NIR and SWIR bands to water help distinguish the effects of soil moisture on mixed soil-vegetation spectra. (2) VRSMIs can provide high-performance SMC estimates under low-moderate vegetation cover (NDVI < 0.55). (3) VRSMIs can be used for cropland SMC evaluation without considering crop VWC (laboratory-based spectra dataset: RMSE = 0.07–0.11, R2 = 0.96–0.98; field-based spectra dataset: RMSE = 0.17–0.18, R2 = 0.63–0.79). Our study indicates that VRSMI and VRSMIs can provide high-performance relative SMC estimates under low-to-moderate vegetation cover.

Agricultural Drought Model Based on Machine Learning Cubist Algorithm and its Evaluation

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province.

Development of maximum relevant prior feature ensemble (MRPFE) index to characterize future drought using global climate models

Drought is one of the foremost outcomes of global warming and global climate change. It is a serious threat to humans and other living beings. To reduce the adverse impact of drought, mitigation strategies as well as sound projections of extreme events are essential. This research aims to strengthen the robustness of anticipated twenty-first century drought by combining different Global Climate Models (GCMs). In this article, we develop a new drought index, named Maximum Relevant Prior Feature Ensemble index that is based on the newly proposed weighting scheme, called weighted ensemble (WE). In the application, this study considers 32 randomly scattered grid points within the Tibetan Plateau region and 18 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) of precipitation. In this study, the comparative inferences of the WE scheme are made with the traditional simple model averaging (SMA). To investigate the trend and long-term probability of various classes, this research employs Markov chain steady states probability, Mann–Kendall trend test, and Sen’s Slope estimator. The outcomes of this research are twofold. Firstly, the comparative inference shows that the proposed weighting scheme has greater efficiency than SMA to conflate GCMs. Secondly, the research indicates that the Tibetan Plateau is projected to experience “moderate drought (MD)” in the twenty-first century.

Comparative analysis of SPI, SPEI, and RDI indices for assessing spatio-temporal variation of drought in Türkiye

AbstractThis research presents a comprehensive drought analysis using climate data obtained from 219 homogeneously distributed meteorological stations in Türkiye between 1991 and 2022. In this context, Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI) drought indices were used and comparative analysis was made. Türkiye. The study demonstrates that below-normal precipitation over extended periods and increasing temperatures have contributed to the increased frequency of meteorological drought events. Türkiye's topographic conditions, particularly its location in the Mediterranean basin, significantly influence drought occurrences. It is noted that over the past 20 years, Türkiye has been trending towards drier conditions, with rising temperatures reinforcing this trend. The study observes that the moderate drought class range is the most frequently recurring in the SPI, SPEI, and RDI methods utilized. Regarding atmospheric conditions affecting the climate in Türkiye, it is observed that increased drought severity stands out prominently in years when the North Atlantic Oscillation is positive. During these years, increased drought severity is evident in the SPI, SPEI, and RDI indices, particularly in winter and autumn, while a wide area experiences drought effects in the summer months. Long-term analyses emphasize that drought periods occur less frequently but have more prolonged impacts, attributed to variations in precipitation patterns from year to year and the influence of rising temperatures due to global climate change. The potential future increase in drought in the Mediterranean basin due to global climate change and Türkiye's vulnerability to this situation could have adverse effects on water resources, food security, energy sources, and ecosystems.

Monitoring agricultural drought using different indices based on remote sensing data in the Brazilian biomes of Cerrado and Atlantic Forest.

Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson's correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region.

Drought evaluation using various evapotranspiration models over semi-arid river basins

ABSTRACT Multivariate drought indices including various hydrological processes into account can be more valuable under climatic and anthropogenic changes. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, considering precipitation, and evapotranspiration (ET) into account. Many studies used empirical, machine learning, and process-based model estimates of ET for the calculation of drought indices of SPEI and SPAEI. However, the sensitivity of ET estimates on drought characteristics at the catchment scale is highly complex. The present study aimed to include and analyze the sensitivity of various approaches of empirical (Budyko, Penman–Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing (MODIS) in the drought characterization using SPEI and SPAEI. The present methodology was tested on a dry-sub-humid river catchment of India, the Tunga-Bhadra River catchment for the period of 2000–2012. The performance of statistical indicators (Nash–Sutcliffe Efficiency and R2) for SPEI values by various empirical methods of Potential Evapotranspiration (PET) (i) Penman–Monteith (ii) Hargreaves against remote sensing PET were 0.93 and 0.95, respectively, which are high in comparison with SWAT simulated PET-based SPEI values, which shows NSE values of 0.85 against remote sensing PET-based SPEI.

Differences in drought characteristics, progression, and recession across ecosystem types in the pantropical region of the Lancang-Mekong River Basin

Exploring the development and impacts of drought across different ecosystems can offer new insights for mitigating the adverse effects of drought events. Using the pantropical Lancang-Mekong River Basin as the study region, we investigated the agricultural, ecological, and hydrological drought characteristics and explored their drought progression and recession rates across four vegetation ecosystem types: tropical forests, subtropical forests, shrubs, and crops. We utilized newly developed drought indices based on the ERA5-Land reanalysis dataset, GOSIF chlorophyll fluorescence data, and modified Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data. The results showed that agricultural and hydrological droughts exhibited increasing trends from 2001 to 2021, whereas ecological drought displayed a decreasing trend over the same period. The cropland region experienced the fewest drought events, shortest drought durations, slowest progression rates, and lowest recession rates. By contrast, the two evergreen, broadleaf forest ecosystems (subtropical and tropical forests) experienced the highest number of drought events and fastest progression and recession rates. The findings suggest a trade-off relationship between vegetation resistance and recovery, where faster drought onset is associated with faster drought recession for ecological drought. Given the more severe challenges posed by agricultural and hydrological droughts, the riparian countries in the Lancang-Mekong River Basin should adopt proactive financial and management measures to mitigate the adverse impacts of these drought types. The insights gained from this study can inform the development of targeted strategies for drought monitoring, preparedness, and response across diverse ecosystems.

A New Hybrid Weighted Regional Drought Index to Improve Regional Drought Assessment

A New Hybrid Weighted Regional Drought Index to Improve Regional Drought Assessment

Droughts and heatwaves in the West African monsoon system

AbstractHeatwaves and droughts increasingly impact public health and societal system in a world subject to global warming. Several studies reported these phenomena all around the world, but there is a dearth of research specifically in West Africa. This study fills that gap by comparing heatwave/heat stress and drought occurrence in three climate zones (Guinea, Sudan and Sahel) of West Africa from 1981 to 2020. The analysis focuses on the comparison of station and gridded datasets. The Cumulative Excess Heat (CumHeat) and the Universal Thermal Climate Index (UTCI) are considered for heatwaves. For drought, the Standardized Precipitation (Evapotranspiration) Index SPI (SPEI) are used at 3‐, 6‐ and 12‐month scales. Both heatwave and drought characteristics are investigated as well as their co‐occurrence (D‐HW). The investigation reveals a good correlation between station and gridded datasets for drought indices. While station data records fewer and less intense heatwave, gridded data indicates longer‐lasting heat extremes. The study also demonstrates a strong agreement between the UTCI computed from the Rayman model and ERA5‐HEAT dataset, despite timing discrepancies, especially along the Guinea coast. The Sahel region is found to endure higher heat stress levels, with increasing intensity of heatwaves over time. Notably, the study uncovers an increasing frequency of compound D‐HW in all zones, especially the Sudan and Sahel zones, offering new insights into the climatic challenges faced by West Africa. These findings emphasize the critical need for improved planning and early warning systems (EWS) to mitigate the impacts of these climate extremes ecosystems and human health.

Comprehensive evaluation of the response relationship between meteorological drought and hydrological drought in the Yalong River Basin, China

Extreme climate events are becoming increasingly severe owing to global warming. In recent years, the Yalong River has experienced frequent droughts, which has seriously hindered the development of hydropower. Based on standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI) values, this study employed transfer entropy, multivariate Copula functions, and a cloud model to study the response relationship and risk situation between meteorological and hydrological droughts. The results showed that: (1) The smaller the scale of SPEI and SRI, the more sensitive the identification of drought and the faster the detection of drought trend changes. (2) Combined with small-scale drought index analyses, the trend and severity of meteorological and hydrological droughts propagating from the upper and middle reaches to the lower reaches is increasing from year to year. (3) The Gumbel-Hougaard Copula function reveals the relationship between meteorological and hydrological drought, with the longest joint return period of 13–15 months. (4) In cloud model risk evaluation, the degree, uncertainty and stochasticity of drought risk in the basin are increasing, and the risk degree of interannual meteorological and seasonal hydrological droughts in the basin was high. This study provides theoretical support for optimizing water resource allocation and drought risk situation analysis in the basin.

A grid-wise approach for accurate computation of Standardized Runoff Index (SRI)

The Standardized Runoff Index (SRI) is a major indicator for evaluating hydrological drought conditions, accomplished by comparing the current runoff data with retrospective runoff conditions of an area for the same period. This hydrological drought indicator facilitates the characterisation of runoff variations across diverse regions. This study introduces a refined methodology for accurate computation of SRI by employing a grid-wise approach. Distinct probability distributions were fitted to each grid within the study area, diverging from the conventional practice of using a single probability distribution for the entire basin or sub-basin. The research endeavours to assess the efficacy of the grid-wise approach in improving the representation of drought characteristics when compared to the traditional areal approach. A comparative analysis between the performances of SRI computed through grid-wise fitting (where the probability distribution dynamically adapts to each grid) and the areal fitting approach (employing a uniform distribution across all grids) was conducted within the Godavari Basin, India. The findings in this study underscore that the misrepresentation of extreme events is inevitable for large heterogeneous basins like Godavari when the traditional areal approach was employed for SRI computation. Consequently, the grid-wise fitting emerges as a more accurate method for computing the SRI, particularly in characterising extreme dry or wet events.

Mountain Peatlands and Drought: Carbon Cycling in the Pyrenees Amidst Global Climate Change

AbstractThis study provides a multi‐year (2017–2022) Net Ecosystem Carbon Balance (NECB) of a Pyrenean mountainous peatland through the integration of field data, satellite imagery, and statistical modeling. Fluvial organic carbon export was measured at 30 min frequency, while gaseous (CO2 and CH4) exchanges were measured monthly using closed chambers. These measurements were combined with Sentinel‐2 derived chlorophyll index and in situ high frequency (1 hr) measurements of key environmental variables such as air temperature, photosynthetically active radiation, and water table level, to develop hourly gaseous carbon flux models (R2 = 0.69 for GPP, R2 = 0.84 for ER, R2 = 0.59 for CH4). Over the 2017–2022 period, modeled average GPP (610 ± 39 gC.m−2.year−1) and ER (641 ± 59 gC.m−2.year−1) showed that the peatland acted as a weak source of CO2 to the atmosphere, releasing 31 ± 73 gC.m−2.year−1. Considering fluvial carbon export and CH4 exchanges, the loss of carbon from the peatland increased to 55 ± 73 gC.m−2.year−1. Dissolved organic carbon constituted 8%–106% of the NECB. The estimated long‐term organic accumulation rate indicated a steady carbon accumulation rate of 16.4 gC.m−2.year−1, contrasting with the contemporary NECB, suggesting a recent shift in ecosystem functioning from a carbon sink to a source. The study underscores the role of water availability and air temperature through a drought index (DI), in shaping the NECB. The DI correlated significantly with annual carbon gaseous fluxes, except for 2022, marked by an intense drought. During this year the peatland became a large source of carbon (189 gC.m−2.year−1) to the atmosphere.

Improvement of drought forecasting by means of various machine learning algorithms and wavelet transformation

AbstractDrought, which is defined as a decrease in average rainfall amounts, is one of the most insidious natural disasters. When it starts, people may not be aware of it, which is why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods, such as drought indices, one of which is the Standardized Precipitation Index (SPI). In this study, SPI was used to detect droughts, and machine learning algorithms, including support vector machines (SVM), artificial neural networks, random forest, and decision tree, were used to predict droughts. In addition, 3 different statistical criteria, which are correlation coefficient (r), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE), were used to investigate model performance values. The wavelet transform (WT) was also applied to improve model performance. One of the areas most impacted by droughts in Turkey is the Konya Closed Basin, which is geographically positioned in the center of the country and is among the top grain-producing regions in Turkey. The Apa Dam is one of the most significant water resources in the area. It provides water to many fertile fields in its vicinity and is affected by droughts which is why it was selected as a study area. Meteorological data, such as monthly precipitation, that could represent the region were obtained between 1955 and 2020 from the general directorate of state water works and the general directorate of meteorology. According to the findings, the M04 model, whose input structure was developed using SPI, various time steps, data delayed up to 5 months, and monthly precipitation data from the preceding month (time t − 1), produced the best results out of all the models examined using machine learning algorithms. Among machine learning algorithms, SVM has achieved the most successful results not only before applying WT but also after WT. The best results were obtained from M04, in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).