Dynamics of meteorological and hydrological drought: The impact of groundwater and El Niño events on forest fires in the Amazon.

Over recent decades, anthropogenic forest fires have significantly altered vegetation dynamics in the Amazon region. While human activities primarily initiate these fires, their escalation is intricately linked to climatic conditions, particularly droughts induced by the warm El Niño phase. This study investigates the impact of meteorological and hydrological drought on forest fires in the Amazon, focusing on the role of groundwater and El Niño events. Utilizing comprehensive drought indicators at various soil depths and standardized precipitation indexes, the research spans from 2004 to 2016, revealing a consistent decrease in humidity conditions across surface soil moisture, root zone soil moisture, and groundwater storage levels. With its slower response to precipitation changes, groundwater emerges as a crucial factor influencing hydrological drought patterns in the Amazon. The spatial distribution of drought conditions is explored, highlighting areas with lower humidity concentrations in the northeast and a correlation between forest fires and positive rates of change in burned area fraction during El Niño events. Notably, the study underscores the substantial increase in burned area during the 2015-2016, characterized by a very strong El Niño. This nuanced understanding of groundwater dynamics and its interplay with El Niño events provides critical insights for developing a tailored fire risk index in the ecologically significant and vulnerable Amazon basin, subsidizing strategies for mitigating fire risk and enhancing preparedness.

Comparative evaluation of multiscaler drought indices under different climatic conditions in Gujarat state of India.

The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), two drought indices, have been compared for interchangeability and reliability under various climatic conditions in Gujarat, India. As the quality of the input is crucial for the accuracy of the index, weather records from surface observatories are preferable over grid and reanalysis data. This study found that on a short timescale (1-3 months), SPEI diagnosed mild and moderate droughts more frequently than SPI, particularly in stations with relatively heavy rainfall. The indices in all timescales at all sites throughout the monsoon months displayed strong positive associations (r > 0.8). The correlation decreases but remains positive as the temporal scale is extended up to 8 months. On a 9-months or longer scales that encompassed active monsoon rainfall months at all stations, correlation coefficients were between 0.8 and 0.9 for all months of the year. During monsoon months, high fractional matches were observed on a short scale. The months after the monsoon show a generalized diagonal pattern of high fractional match with the timescale for all stations. The kappa statistic followed a broad pattern comparable to the match fractions. The instances with poor agreements (R, Match and kappa < 0.3) had proportional bias between the indices. SPEI recognized more drought events at all stations in the short time periods, while the agreements increased with longer time scales. However, SPI detects high intensities in the subhumid.

Exploring climate shifts in the Ganga-Brahmaputra basin based on rainfall and temperature variability.

Climate change has a significant impact on the Ganga-Brahmaputra (GB) basin, the major food belt of India, which frequently experiences flooding and varied incidences of drought. The current study examines the changing trend of rainfall and temperature in the GB basin over a period of 30 years to identify areas at risk with an emphasis on the Paris Agreement's mandate to keep increasing temperatures below 2 °C. The maximum temperature anomaly in the middle Ganga plains recorded an increase of more than 1.5 °C year-1 in 1999, 2005, and 2009. Some extreme events were observed in the Brahmaputra basin during 1999, 2009, and 2010, where a prominent temperature increase of 1.5 °C year-1 was observed. The minimum temperature revealed an increasing trend for the G-B basin with an anomalous increase of 0.04 to 0.06 °C year-1. The rainfall variability across the Ganga basin shows a rising tendency over the lower Ganga region while the Brahmaputra basin showed a downward trend. To identify the statistical relation between the Global climatic oscillations and regional climate, Standardized Precipitation Index (SPI) and Niño 3.4 were used. The wet and dry period estimation shows a rise in flood conditions in the Ganga basin whereas, in the Brahmaputra basin, an increase in drought frequency was observed. The correlation based on Niño 3.4 and SPI3 presents a negative relation for the monsoon season in the G-B basin revealing a situation of drought occurrence (SPI3 below 0) with increased Nino 3.4 values (El Niño above + 0.4C).

Spatio-temporal characterization of drought variability in data-scarce regions using global precipitation data: a case study in Cauto river basin, Cuba.

Drought is considered the most severe water-related disaster in the Cauto river basin, which is the longest river and the main agricultural producer in Cuba. Better understanding of drought characteristics is crucial to drought management. Given the sparsity of ground-based precipitation observations in the Cauto, this study aims at using gridded global precipitation to analyze the spatio-temporal variations of drought in this river basin. Firstly, the monthly Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was calibrated with the gauged precipitation using the Thiessen polygon-based method and linear least squares regression equations. Then, the gridded standardized precipitation index (SPI) with time scales of 3, 6, 9 months and drought characteristics, namely, drought frequency, duration and intensity were calculated using the calibrated CHIRPS. Finally, the spatio-temporal analysis was performed to investigate the variations of drought in the Cauto river basin in time and space. The obtained results show that the calibrated CHIRPS is highly consistent with the gauged observations and is capable of determining the magnitude, time, and spatial extent of drought events in the Cauto river basin. The trend analysis by the Mann-Kendall test reveals that although the trend is not statistically significant, the SPI tends to decrease with time in the dry season, which indicates the more severe drought. The spatial analysis indicates that the lower altitude area of the Cauto river basin is suffered from longer drought duration and higher drought intensity than the upper one. This study expresses the importance of open global precipitation data sources in monitoring and quantifying drought characteristics in data-scarce regions.

Effects of interaction of multiple large-scale atmospheric circulations on precipitation dynamics in China.

Different scenarios of precipitation, that lead to such phenomena as droughts and floods are influenced by concurrent multiple teleconnection factors. However, the multivariate relationship between precipitation indices and teleconnection factors, including large-scale atmospheric circulations and sea surface temperature signals in China, is rarely explored. Understanding this relationship is crucial for drought early warning systems and effective response strategies. In this study, we comprehensively investigated the combined effects of multiple large-scale atmospheric circulation patterns on precipitation changes in China. Specifically, Pearson correlation analysis and Partial Wavelet Coherence (PWC) were used to identify the primary teleconnection factors influencing precipitation dynamics. Furthermore, we used the cross-wavelet method to elucidate the temporal lag and periodic relationships between multiple teleconnection factors and their interactions. Finally, the multiple wavelet coherence analysis method was used to identify the dominant two-factor and three-factor combinations shaping precipitation dynamics. This analysis facilitated the quantification and determination of interaction types and influencing pathways of teleconnection factors on precipitation dynamics, respectively. The results showed that: (1) the Atlantic Multidecadal Oscillation (AMO), EI Niño-Southern Oscillation (ENSO), East Asia Summer Monsoon (EASM), and Indian Ocean Dipole (IOD) were dominant teleconnection factors influencing Standardized Precipitation Index (SPI) dynamics; (2) significant correlation and leading or lagging relationships at different timescales generally existed for various teleconnection factors, where AMO was mainly leading the other factors with positive correlation, while ENSO and Southern Oscillation (SO) were mainly lagging behind other factors with prolonged correlations; and (3) the interactions between teleconnection factors were quantified into three types: enhancing, independent and offsetting effects. Specifically, the enhancing effect of two-factor combinations was stronger than the offsetting effect, where AMO + NAO (North Atlantic Oscillation) and AMO + AO (Atlantic Oscillation) had a larger distribution area in southern China. Conversely, the offsetting effect of three-factor combinations was more significant than that of the two-factor combinations, which was mainly distributed in northeast and northwest regions of China. This study sheds new light on the mechanisms of modulation and pathways of influencing various large-scale factors on seasonal precipitation dynamics.

Future climate-driven drought events across Lake Urmia, Iran.

Climate change has increased the vulnerability of arid and semi-arid regions to recurrent and prolonged meteorological droughts. In light of this, our study has sought to assess the nature of future meteorological drought in Lake Urmia basin, Iran, within the context of future climate projections. To achieve this, data from 54 general circulation models (GCMs) was calibrated against both in situ and Global Precipitation Climatology Centre datasets. These GCMs were then employed to project drought conditions expected over 2016-2046 under RCP2.6 and RCP8.5 as the most optimistic and pessimistic scenarios, respectively. To provide a comprehensive analysis, these RCPs were combined with two different time scale Standardized Precipitation Index (SPI), leading to eight different scenarios. The SPI was calculated over two temporal scales for the past (1985-2015) and future (2016-2046), including the medium-term (SPI-6) and long-term (SPI-18) index. Results showed that while precipitation is expected to increase by up to 34%, parts of the basin are projected to face severe and prolonged droughts under both RCPs. The most severe drought event is expected to occur around 2045-2046 under the most pessimistic RCP8.5 scenario. Severe droughts with low frequency are also anticipated to increase under other scenarios. By characterizing meteorological drought conditions for Lake Urmia basin under future climate conditions, our findings call for urgent action for adaptation strategies to mitigate the future adverse effects of drought in this region and other regions facing similar challenges. Overall, this study provides valuable insight into the impacts of climate change on future droughts that can adversely influence water resources in arid and semi-arid regions.

A generalized procedure for joint monitoring and probabilistic quantification of extreme climate events at regional level.

Droughts and heat waves are currently recognized as two of the most serious threats associated with climate changes. Drought is characterized by prolonged dry periods, low precipitation, and high temperature, while heat wave refers to an extended period of exceptionally high temperature, surpassing the region's average for that time of year. There is a close relationship between droughts and heat waves, as both are often caused by similar weather patterns and can exacerbate each other's impacts. Therefore, it is crucial to monitor and quantify both droughts and heat waves jointly at a regional level in order to develop sustainable policies and effectively manage water resources. This article develops a new index, the standardized composite index for climate extremes (SCICE), for joint monitoring and probabilistic quantification of extreme climate events at regional level. The procedure of SCICE is mainly based on the joint standardization of standardized precipitation index (SPI) and standardized temperature index (STI). In the application of SCICE, results reveal that the long-term probabilities of the joint occurrence of dry and hot events are significantly greater than those of wet and cold events. Furthermore, the outcomes of the comparative assessment support the validity of using SCICE as a compact statistical approach in regional drought analysis. In summation, the study demonstrates the capability of SCICE to effectively characterize and assess the joint monitoring of drought and heat waves at a regional level, providing a comprehensive approach to understanding the joint impact of climate extremes.

The impacts of global atmospheric circulations on the water supply in select watersheds in the Indonesian Maritime Continent using SPI.

Despite the diverse atmospheric circulations affecting the Indonesian Maritime Continent (IMC), i.e., El Nino Southern Oscillation-ENSO, Indian Ocean Dipole-IOD, Madden Julian Oscillation-MJO, Monsoon, there is a lack of research on their interaction with hydrological events in watersheds. This study fills this gap by providing insights into the dominant atmospheric events and their correlation with the water supply in three characteristic watersheds, i.e., Tondano (north/Pacific Ocean), Jangka (south/Indian Ocean), and Kapuas (equatorial/interior) in IMC. The research used the standardized precipitation index for the 1-monthly (SPI1), 3-monthly (SPI3), and 6-monthly (SPI3) scale generated from 23 years (2000-2022) of monthly historical satellite rainfall data. The analysis compared each location's SPI indices with the monthly Nino 3.4, Dipole Mode Index (DMI), MJO (100E and 120E), Monsoon index, and streamflow data. The result shows that the dominant atmospheric events for the Tondano watershed were ENSO, IOD, and MJO, with correlation values of -0.62, -0.26, and -0.35, respectively. The MJO event was dominant for the Kapuas watershed, with a correlation value of -0.28. ENSO and IOD were dominant for the Jangka watershed, with correlation values of -0.27 and -0.28, respectively. The monsoon correlated less with the SPI3 in all locations, while it modulates the wet and dry period pattern annually. Most intense dry periods in Tondano occur with the activation of El Nino, while the intense wet period occurs even in normal atmospheric conditions. Most intense wet periods in Jangka occur with the activation of La Nina, while the intense dry period occurs even in normal atmospheric conditions. The occurrence of MJO compensates for the intense wet and dry periods in Kapuas. The correlation among SPI3, atmospheric circulation, and streamflow in the diverse watershed characteristics in the IMC watersheds could give strategic information for watershed management and applies to other watersheds with similar atmospheric circulation characteristics.

Association of farmers' wellbeing in a drought-prone area, Thailand: applications of SPI and VCI indices.

Drought has major consequences for agricultural activity, economy, and the environment. For improved drought management, it is necessary to assess drought severity, frequency, and the potential of drought occurrence. The purpose of this study is to use drought indices, standardized precipitation index (SPI), and vegetation condition index (VCI) to characterize the severity of drought and investigate the association between drought severity and subjective well-being among local farmers. The SPI was used to quantify precipitation deficits at various time scales, while the VCI was utilized to monitor crop and vegetation drought conditions. Throughout the period 2000-2017, satellite data were incorporated, as well as a household survey of rice farmers in the dry zone research region in northeastern Thailand. The findings suggest that extreme droughts occur more frequently in the central part of the northeastern region of Thailand than in the rest of the region. The influence of drought on farmers' wellbeing was evaluated at various drought severity levels. Drought and overall wellbeing are strongly linked at the household level. Thai farmers in drought-prone areas are dissatisfied with their livelihoods more than farmers in less-affected area. It is intriguing that farmers who live in drought-prone areas are more content with their lives, their communities, and their occupations than farmers who live in less drought-prone areas. In this context, using proper drought indices could potentially improve the utility of governmental interventions and community-based programs targeted at assisting drought-affected people.

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models.

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.

Trends in drought and effects on carbon sequestration over the Chinese mainland.

Recently, drought events have occurred frequently and have profoundly altered the carbon sequestration in terrestrial ecosystems. How drought affects carbon sequestration is an important issue which may assist in understanding and confronting the challenges of extreme climate change. Nevertheless, drought-induced carbon-cycle effects remain scarce from the perspective of drought indices. In this study, we quantified the impacts of potential evapotranspiration (PET), standardized precipitation evapotranspiration index (SPEI), downward short-wave radiation flux (SWDown), and soil water (Soil_w) on net ecosystem productivity (NEP). We showed that the spatiotemporal heterogeneity of drought was extremely significant, and the hot spots of aridification were mainly distributed in the southwestern Yungui Plateau (YG) and Northwest China (NW). Moreover, the "pan evaporation paradox" appeared across the Chinese mainland before the 1990s and subsequently disappeared. Similarly, in contrast to the moderate NEP fluctuation between 1981 and 1999, since the beginning of the 21st century, NEP has increased significantly across Chinese mainland, YG, the plains region of Changjiang (CJ), and Southeast China (SE). Meanwhile, there are obvious directional, temporal, and spatial differences in the effects of the drought indices on NEP. Specifically, a higher SPEI value results in a more obvious promoting effect on NEP in SE, North China (NN), and northeastern YG. An increase in SWDown can promote an increase in NEP, especially in the northeastern YG and central SE. The increase in Soil_w in parts of the Qinghai-Tibetan Plateau, Xinjiang Region (XJ), southeastern NW, NN, and Northeast China with poor water conditions can promote carbon sinks. The inhibition effect is particularly obvious in some areas of CJ, where water resources are abundant. The fluctuation in PET has a relatively low influence on NEP. This study provides a comprehensive assessment of drought change and its impact on carbon sequestration and may help in formulating appropriate policies for carbon management and ecological security.

Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland.

The primary aim of this work is to assess the accuracy of the methods for spatial interpolation applied for the reconstruction of the spatial distribution of the Standardized Precipitation Index (SPI). The one-month version called SPI-1 is chosen for this purpose due to the known greatest variability of this index in comparison with its other versions. The analysis has been made for the territory of the entire country of Poland. At the same time the uncertainty related to the application of such computational procedures is determined based on qualitative and quantitative measures. The public data of two kinds are applied: (1) measurements of precipitation and (2) the locations of the meteorological stations in Poland. The analysis has been made for the period 1990-2020. However, all available observations since 1950 have been implemented. The number of available meteorological stations has decreased over the analyzed period. In January 1990 there were over one thousand stations making observations. In the end of the period of the study, the number of stations was below six hundred. Obviously, the temporal scarcity of data had an impact on the obtained results. The main tools applied were ArcGIS supported with Python scripting, including generally used modules and procedures dedicated to geoprocessing. Such an approach appeared crucial for the effective processing of the large number of data available. It also guaranteed the accuracy of the produced results and brought about drought maps based on SPI-1. The methods tested included: Inverse Distance Weighted, Natural Neighbor, Linear, Kriging, and Spline. The presented results prove that all the procedures are inaccurate and uncertain, but some of them provide satisfactory results. The worst method seems to be the interpolation based on Spline functions. The practical aspects related to the implementation of the methods led to removal of the Linear and Kriging interpolations from further use. Hence, Inverse Distance Weighted, as well as Natural Neighbor, seem to be well suited for this problem.

Wetland characteristics affect abundance and diversity of wintering birds: A case study in South-Western Iran.

Water availability is an important driver of bird population change, and its effects are likely to increase in coming decades under climate change. Here we assess effects of temperature, precipitation, and water area on wintering bird populations in Miyangaran Wetland in southwestern Iran. Modeling methods including, generalized linear model (GLM) and hierarchical partitioning were used to examine the relative importance of variables. The number of wintering species, inhabiting the wetland, varied among years, ranging from 10 to 48 species. The total number of wintering birds showed a significant decreasing trend. A significant increasing trend was obtained for shorebirds, while waterfowl species were significantly decreased. The GLM showed that species abundance, richness, and diversity were significantly correlated with the standardized precipitation index (SPI), annual precipitation, and normalized difference water index (NDWI). Hierarchical partitioning analysis also identified NDWI, SPI, and annual precipitation as the most important variables with average independent effects of 35, 36 (p < .01) and 17% (p < .05), respectively. Our results revealed that the water area plays a major role in determining the structure of bird diversity and abundance, affecting both waterfowl and shorebirds.

Determining the most appropriate probability distribution function for meteorological drought indices in Urmia Lake Basin, Iran.

Normalization is believed to be one of the most important parts of numerical computation in discrete mathematics. This process aims to transform a wide numerical range into a narrower one. Hence, in a number of fields of study, numerous distribution functions (DF) have been extended based on their applications, one of which is drought calculation. In this research, annual drought was calculated via standard precipitation index (SPI) and China Z Index (CZI) through seven three-parametric DFs (Pearson 5, Weibull, Pearson 3 (gamma), log Pearson, Fréchet, log-logistic, and fatigue life) in order to determine the most appropriate one for each index in Urmia Lake Basin. To this end, the results of both SPI and CZI, with DFs and without them, were compared with statistical analyzers (RMSE, ME, R2, and pearson correlation). The results indicated that Weibull-CZI and Pearson 5-SPI had the highest correlation with the normal ones. Therefore, they could be used as the best DFs for these drought indices in this basin. Moreover, among the studied years, Gelazchay and Daryanchay stations experienced the most severe drought in 2008 and 1999 based on the CZI and SPI, respectively. It should be noted that in another section of the current study, the correlation between the two indices was analyzed and the results showed high correlations between them.

Spatio-temporal drought assessment of the Subarnarekha River basin, India, using CHIRPS-derived hydrometeorological indices.

Precipitation studies have a crucial role in deciphering climate change and monitoring natural disasters such as droughts. Such studies lead to better assessment of rainfall amounts and spatial variabilities; and have a vital role in impact assessment, mitigation, and prediction of occurrence. Thus, this study has been undertaken in the Subarnarekha River basin using Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset. Precipitation datasets helped in deriving hydrometeorological indices such as the Rainfall Anomaly Index (RAI) and Standardized Precipitation Index (SPI) for the identification of drought occurrences. The core objective was to infer spatio-temporal drought scenarios and their trend characterization covering four decades over the years 1981 to 2020. Quantitative drought assessment was done using run theory for identifying the Drought Duration (DD), Drought Severity (DS), Drought Intensity (DI), and Drought Frequency (DF). Mann-Kendall (MK) test was performed to understand the precipitation and drought trends at annual and seasonal scales. Eight severe drought events were identified in the Subarnarekha River basin for the past 40 years and the average DI value of 0.8 was recorded. MK test results for the precipitation showed a significant positive trend (95% confidence level) for pre-monsoon periods. However, for SPI, a significant positive trend was observed over the intervals of 3 (SPI3), 6 (SPI6), and 12 (SPI12) months respectively at an annual timescale, suggesting wetter conditions within the study area. Moreover, there had been insignificant negative trends for SPI1 and SPI3 during winter. It indicates that during the short-term SPI scale, i.e., 1 month (SPI1) and 3 months (SPI3), the instances of negative SPI values inferred were high, which point to the increasing incidences of meteorological drought possibly due to deficient soil moisture. Thus, the results indicated that the CHIRPS precipitation product-derived hydrometeorological indices could act as a valuable tool for assessing the past spatio-temporal drought conditions of the Subarnarekha River basin. This may further be helpful in planning for sustainable water resource management of such river basins.

The impact of climate change on budget balances and debt in the Middle East and North Africa (MENA) region.

Lower tax revenues and greater government spending result in higher deficits and public debt. As a result, determining the degree of budgetary effects is vital, but important to assess the persistence of these effects. We aim to investigate the impact of climate change on the fiscal balance and public debt in the countries of the Middle East and North Africa. The empirical analysis relies on panel data in the period 1990-2019 and employs various models. The findings show that temperature changes adversely affect the government budget and increase debt, but we find no significant impact of changes in rainfall. The average temperature decreases fiscal balance by 0.3 percent and increases debt by 1.87 percent. Using projections of temperature and rainfall over the years 2020 to 2099, we find a significant decrease in the fiscal balance at 7.3 percent and an increase in the public debt at 16 percent in 2060-2079 and 18 percent in 2080-2099 under the assumption of a high greenhouse gas (GHG) emission scenario. On the contrary, under the low GHG emission scenario, the fiscal balance deteriorates by 1.7 percent in 2020-2039 and 2.2 percent in 2080-2099, while public debt rises by 5 percent in 2020-2039 and 6.3 percent in 2080-2099. Supplementary Information: The online version contains supplementary material available at 10.1007/s10584-022-03388-x.

A new comprehensive approach for regional drought monitoring.

The Standardized Precipitation Index (SPI) is a vital component of meteorological drought. Several researchers have been using SPI in their studies to develop new methodologies for drought assessment, monitoring, and forecasting. However, it is challenging for SPI to provide quick and comprehensive information about precipitation deficits and drought probability in a homogenous environment. This study proposes a Regional Intensive Continuous Drought Probability Monitoring System (RICDPMS) for obtaining quick and comprehensive information regarding the drought probability and the temporal evolution of the droughts at the regional level. The RICDPMS is based on Monte Carlo Feature Selection (MCFS), steady-state probabilities, and copulas functions. The MCFS is used for selecting more important stations for the analysis. The main purpose of employing MCFS in certain stations is to minimize the time and resources. The use of MCSF makes RICDPMS efficient for drought monitoring in the selected region. Further, the steady-state probabilities are used to calculate regional precipitation thresholds for selected drought intensities, and bivariate copulas are used for modeling complicated dependence structures as persisting between precipitation at varying time intervals. The RICDPMS is validated on the data collected from six meteorological locations (stations) of the northern area of Pakistan. It is observed that the RICDPMS can monitor the regional drought and provide a better quantitative way to analyze deficits with varying drought intensities in the region. Further, the RICDPMS may be used for drought monitoring and mitigation policies.

Spatiotemporal Response of Hydrological Drought to Meteorological Drought on Multi-Time Scales Concerning Endorheic Basin.

Under the controversial background of "Northwestern China is gradually developing towards warm and humid", how hydrological drought responds to meteorological drought at the endorheic basin is of great significance. To address this problem, we first analyzed the spatiotemporal variation of meteorological and hydrological droughts at Tarim Basin River from 1960 to 2014 by using the daily standardized precipitation index (SPI) and daily standardized terrestrial water storage index (SWSI) based on the reanalysis data. Thereafter, we explored the spatiotemporal response of hydrological drought to meteorological drought on the multi-time scale by using the cross-wavelet transform method, Ensemble Empirical Mode Decomposition (EEMD), and correlation analysis. We find that: (1) both meteorological and hydrological droughts signified a gradually weakened trend in time; (2) meteorological and hydrological drought have significant resonance periods on the 10-month time scale and the 8-year time scale; (3) hydrological drought generally lags behind the meteorological drought by 7 days in plains areas, while it can last as long as several months or even a year in mountainous areas.

Spatiotemporal drought monitoring using bottom-up precipitation dataset (SM2RAIN-ASCAT) over different regions of Iran.

Drought is a natural phenomenon that can significantly impacts on water resources studies, agricultural and environmental societies around the world, hence, accurate spatio-temporal monitoring of drought is very important. In this research, a comparative analysis of a newly developed precipitation dataset, SM2RAIN-ASCAT (which is based on bottom-up approach), with 40 ground-measured Iranian Meteorological Organization (IMO) precipitation data are performed to estimate the precipitation and monitor the drought events over diverse climate regions of Iran. The SPI index, as a widely used drought index, at the temporal resolution ranging from one month to one year is used to this aim, and the outputs are analyzed based on the statistical and categorical metrics. Results indicated that the highest correlation coefficient (CC) and lowest root mean square error (RMSE) between SM2RAIN-ASCAT and in situ observations are found at 10-day and monthly time scales. Analyzing both datasets using FAR and POD indices in the mid and long-term time scales indicated that the SM2RAIN-ASCAT has a good performance in detecting rainy days. This product overestimate the precipitation values in extra-arid regions, while in humid and per-humid climate areas it tends to underestimation. Moreover, assessing the reliability of this product for drought monitoring showed that the SPI at 1, 3 and 6 month time scales are in good agreement with ground-based observations over different climate regions of Iran. At these temporal resolutions, the CC value between SPIs calculated based on in situ observations and SM2RAIN-ASCAT is higher than 0.7 in more than 75% of the meteorological stations. The efficiency of SM2RAIN-ASCAT in detecting drought periods in extra-arid and arid zones is relatively better than that of in humid and per-humid climates. In addition, the performance of this product for capturing wet periods in extra-arid to semi-arid regions is better than that of in Mediterranean and humid zones. Overall, the outcomes of this study demonstrated that SM2RAIN-ASCAT, despite poor performance in estimating precipitation in some regions, can be considered as a complementary to ground-gauge observations or an appropriate alternative dataset for drought analysis, especially in arid and semi-arid regions which include most parts of the world.

Caracterização da seca histórica da década de 2010 na Bacia do Rio Paraíba do Sul, Estado de São Paulo, Brasil / Characterization of the 2010-decade historical drought in Paraiba do Sul River Basin, São Paulo State, Brazil

RESUMO A Bacia Hidrográfica do Rio Paraíba do Sul foi afetada por uma das secas mais severas de sua história, durante os anos de 2013 a 2015, que resultou em várias consequências para o gerenciamento de seus recursos hídricos. O objetivo deste artigo foi contribuir com o entendimento desse evento histórico, no trecho Paulista da Bacia do Rio Paraíba do Sul, por meio do cálculo e da análise do índice padronizado de precipitação. Esse índice foi calculado nas escalas de 1, 3, 6 e 12 meses, para 22 estações pluviométricas localizadas na área de estudo. Os resultados demonstraram que a seca ocorrida entre 2013 e 2014 foi uma das mais severas já registradas, e que o verão de 2014 foi o período crítico no que diz respeito à redução da chuva regional. Essa seca impactou o armazenamento e a capacidade de regularização do principal reservatório da região, o Reservatório de Paraibuna. Embora o pico da crise hídrica tenha ocorrido, principalmente, em razão da seca de 2013/2014, verificou-se que ele foi influenciado pelo efeito cumulativo de uma seca anterior, ocorrida desde o início do ano de 2011. Esse resultado reforça a importância do planejamento plurianual da operação do Sistema Hidráulico da Bacia do Rio Paraíba do Sul.

ABSTRACT The Paraiba do Sul River Basin was affected by one of the most severe drought periods in its recent history, during the years 2013 to 2015, which resulted in several consequences for the management of its water resources. This article aims to contribute to the understanding of this historical event in the São Paulo Stretch of the Paraiba do Sul River Basin, through the calculation and analysis of the standardized precipitation index (SPI). SPI was calculated for the time scales of 1, 3, 6, and 12 months, for 22 rainfall stations located in the study area. The results showed that the drought occurred between 2013 and 2014 was one of the most severe ever recorded, and that the summer of 2014 was the critical period in terms of reduction of regional rainfall. This drought impacted the storage and regularization capacity of the main reservoir of the region, the Paraibuna Reservoir. Although the peak of the water crisis occurred mainly due to the 2013/2014 drought, it was found that it was influenced by the cumulative effect of a previous dry period, that occurred since the beginning of the year of 2011. This result reinforces the importance of multiannual planning for the operation of the Paraíba do Sul River Basin Hydraulic System.