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The impact of the Madden-Julian Oscillation (MJO) on India's intraseasonal and interannual variability of rainfall has been investigated in this study. Anomalous MJO amplitude points from 1979 to 2021 are classified into post-, pre- and normal onset years of the Indian Summer Monsoon (ISM). High MJO amplitudes, particularly those exceeding three standard deviations from the mean, correlate with significant rainfall anomalies, affecting both the timing and intensity of the monsoon. Specific MJO phases contribute differently to rainfall patterns, with phases 3, 4, and 5 generally suppressing rainfall, while phases 1 and 8 enhance it. Pre-onset years show significant rainfall anomalies driven by strong MJO activity, particularly during phases 3, 4, and 5. Normal onset years exhibit notable suppression and enhancement patterns, with significant fluctuations observed in 1996 and 2020. Post-onset years have the highest frequency of anomalies, with phases 3 and 4 prominently influencing rainfall variability. Overall, this research underscores the critical role of MJO dynamics in modulating rainfall variability in India. The findings suggest that understanding MJO phases can improve rainfall predictability and monsoon onset forecasting, aiding in better climate prediction and water resource management. This study contributes to the broader climatological discourse by elucidating the interplay between MJO activity and regional precipitation patterns. [ABSTRACT FROM AUTHOR]

BACKGROUND: The mechanisms that regulate multi‐annual population dynamics of rodent pest species of cereal crops is often unknown. Better knowledge of such aspects can aid pest management and in turn improve food security and human health. The patterns and processes of the population dynamics of Rattus argentiventer, in rice fields of Indonesia, and Rattus tanezumi, in rice fields of the Philippines were assessed in this article. RESULTS: The meta‐analysis of trapping data over 20 years in Indonesia, and 16 years in the Philippines indicated that rodent populations in rice fields did not show a regular multi‐annual pattern. Rattus argentiventer populations in Indonesia responded to less rainfall from the current year. Rattus tanezumi populations in the Philippines responded positively to both rainfall and rainfall anomaly with a 1‐year time lag. CONCLUSIONS: Our study of long‐term population data indicates that certain combinations of rainfall parameters could be useful to predict years when there is higher rodent abundance in rice fields. The key rodent pest species in rice fields in Indonesia (R. argentiventer) and the Philippines (R. tanezumi) differ, and the populations of each species respond differently to rainfall anomalies. Other factors such as crop cover and water availability may also be important and should be considered in future work. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In August 2022, Pakistan experienced unprecedented monsoon rains, leading to devastating floods and landslides affecting millions. While previous research has mainly focused on the contributions of seasonal and synoptic anomalies, this study elucidates the dominant influences of tropical and extratropical intraseasonal oscillations on both the occurrence and subseasonal prediction of this extreme rainfall event. Our scale-decomposed moisture budget analysis revealed that intense rainfall in Pakistan was triggered and sustained by enhanced vertical moisture transport anomalies, primarily driven by interactions between intraseasonal circulation anomalies and the prevailing background moisture field when tropical and mid-latitude systems coincided over Pakistan. Evaluation of subseasonal-to-seasonal prediction models further highlighted the critical role of tropical intraseasonal modes in causing this extreme rainfall event in Pakistan. Models that accurately predicted northward-propagating intraseasonal convection with a forecast lead time of 8–22 days demonstrated good skill in predicting the extreme event over Pakistan. [ABSTRACT FROM AUTHOR]

ABSTRACT Attributions of rainfall anomalies to weather systems and their spatio‐temporal variability in Victoria, southeast Australia are investigated with a multimethod weather type dataset and two popularly used gridded daily rainfall datasets for the period 1979–2015. The rainfall anomalies before, during and after the Millennium Drought (1997–2009) are compared to quantify the temporal variability of rainfall responses to weather type changes. The results show: (1) Three weather systems (Front, Cyclone and Thunderstorm) and their combinations contribute 89% of total rainfall; (2) Contributions of weather types to rainfall vary from month to month with winter season rainfall coming from more diverse weather types than summer rainfall; (3) The contributions of weather types to rainfall in three periods show temporal variabilities and there is a clear shift of contribution pattern after the Millennium Drought, such as front‐thunderstorm (FT) is now the largest contributor to rainfall compared with cyclone‐frontal‐thunderstorm (CFT) before and during the Millennium Drought; (4) A seasonal shift in the post‐drought period is found with higher rainfall in February and March and lower rainfall in September and October. The increased rainfall in February mainly results from Front–Thunderstorm (FT) and Thunderstorm‐only (TO), while rainfall declines in September from all weather types; (5) Several rainfall characteristics that are important for streamflow generation, such as rainfall intensity, probability of rainfall occurrence, number of rainfall days and the maximum daily rainfall, do depend on the weather types; (6) The results are similar with different rainfall datasets, but differences do exist, especially at the local scale. The conclusions of this study are drawn from an Australian case study but have implications for other regions to investigate the attributions of rainfall characteristic changes to weather systems. [ABSTRACT FROM AUTHOR]

Drought is a recurring natural hazard impacting agriculture, water resources, and various socioeconomic sectors. This study evaluated the performances of multiple meteorological and hydrological drought index estimation methods in the Tekeze River basin, northwestern Ethiopia. Monthly rainfall and temperature data from 15 meteorological stations with varying record lengths (28–59 years) and streamflow data from nine functional stations (1991–2018) were used to compute hydrometeorological drought indices. Standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), reconnaissance drought index (RDI), rainfall anomaly index (RAI), decile index (DI), and streamflow drought index (SDI) were calculated using DrinC software, R programming, and empirical formulas at 1, 3, 6, and 12‐month timescales. Results from the analysis revealed a basin‐wide prevalence of mild drought, with SPI indicating severity and negative values over longer timescales. The RDI analyses showed lower drought severity but also decreased its frequency over longer timescales. The DI calculation using basin station averages indicated 48.4%, 10.7%, and 8.0% incidence rates for severe, moderate, and mild droughts, respectively. RAI had generally lower positive and negative values compared to SPI. SPEI‐1 analysis classified almost all stations as experiencing mild drought. Conversely, SDI6 analysis at a 6‐month timescale revealed diverse impacts, with the Tekeze River basin experiencing sustained severe drought (SDI6 < −2.0) for three consecutive years (2004–2006). Spearman's rank correlation coefficients of the indices were positive, with RAI showing particularly strong and statistically significant positive correlations with other indices. This study provides valuable insights for policymakers, researchers, and water resource managers in the Tekeze River basin, aiding in the development of effective drought mitigation and preparedness strategies. [ABSTRACT FROM AUTHOR]

Before establishment of the Asian summer monsoon, the spring diabatic heating over the Tibetan Plateau (TP) is regarded as an essential factor to modulate rainfall in the surrounding and downstream regions of the TP, but its intraseasonal variability (ISV) has not been paid enough attention to. Based on the observation and ERA5 reanalysis data, the first two leading modes of the spring ISV of the surface sensible heat (SH) and the latent heat of condensation (LH) over the eastern TP have been identified, which both exhibit a north–south dipole and uniform pattern with dominant ~ 40-day and 10–20-day periods, respectively. The ISV of LH is strongly negatively correlated to that of SH, which is determined by variations of ground-air temperature difference. The north–south dipole patterns for the 40-day period are induced by the synergistic effect of northward-propagating circulation anomalies from the tropical Indian Ocean and a southeastward-propagating wave train in the middle-to-high latitudes, whereas the uniform patterns can be attributed to an eastward-propagating zonal wave train in the mid-latitude. Accompanied with the ISV of SH and LH, the anomalous moisture convergence/divergence and thus rainfall anomalies occur in the Yangtze River Basin. These results suggest that the spring diabatic heating over the eastern TP has great potentials as a predictability source of anomalous precipitation in its downstream regions on intraseasonal timescale. [ABSTRACT FROM AUTHOR]

Uganda in East Africa is experiencing highly variable rainfall which is exacerbated by temperatures warming at faster rates. This study analyzed rainfall and temperature patterns in comparison with the potential evaporation transpiration rates (PETs) for parts of Central, Western, Southern, and Southwestern Uganda for varying periods from 1981 to 2022. For rainfall onset date (OD), threshold of 0.85 mm for a rainy day, rainfall of 20 mm accumulated over 5 days with at least 3 rain days, and dry spell not exceeding 9 days in the next 30 days were used. The rainfall cessation dates (RCDs) are determined when water balance (WB) falls below 5 mm in 7 days in the last month of the expected season (May and December) for the first and second season, respectively. Standardized rainfall anomaly was utilized to show seasonal and annual rainfall variability. Pearson's correlation (r) coefficient was used to show the relationship between weather variables (rainfall, temperature) and PET at five rainfall stations. Results showed highly varied onset and cessation dates for March–May (MAM) seasonal rainfall compared to those of September–December (SOND). Results showed highly variable onset and cessation of rainfall over the region and statistically significantly increasing trends in both maximum and minimum temperatures across the region, with the highest rate of increase of maximum and minimum temperature of 0.70 and 0.65 °C per decade respectively. Moreover, the maximum temperature and PET showed strong positive correlation coefficient (r) that ranged from 0.76 to 0.90 across the regions, which likely contribute to excess evaporation from the surfaces, soil moisture deficits that negatively affect plant biomass production, low crop yields and food insecurity. PET and rainfall revealed insignificant statistical negative correlation as indicated by the correlation coefficient ranging from − 0.04 to − 0.22. We recommend water management and conservation practices such as mulching, zero tillage, agroforestry, planting drought-resistant crops, and using affordable irrigation systems during period of water deficit. [ABSTRACT FROM AUTHOR]

The Arborea plain in Sardinia (Italy) is classified as a nitrate vulnerable zone (NVZ). In the present study, the individual work steps that are necessary to progress from the existing 3D hydrogeological model to a 3D numerical groundwater model using the interactive finite-element simulation system FEFLOW 7.4 are shown. The results of the transient flow model highlight the influence of the drainage network on the overall groundwater management: the total water volume drained by the ditches accounted for approximately 58% of the annual outflow volume. The numerical transport simulations conducted from 2012 to 2020 using hypothetical field-based nitrate input scenarios globally underestimated the high concentrations that were observed in the NVZ. However, as observed in the field, the computed nitrate concentrations in December 2020 still varied strongly in space, from several mg L−1 to several hundreds of mg L−1. The origin of these remaining local hotspots is not yet known. The modeling of rainfall fluctuations under the influence of climate change revealed a general long-term decline in the groundwater level of several tens of centimeters in the long term and, in conjunction with a zero-nitrate scenario, led to a significant decrease in nitrate pollution. Although hotspots were attenuated, the concentrations at several monitoring wells still exceeded the limit value of 50 mg L−1. [ABSTRACT FROM AUTHOR]

Agriculture in Ethiopia is highly dictated by spatial patterns and temporal distributions of climate variables. The analysis of these climate variables is crucial for understanding the impacts on agricultural productivity. This study aimed to analyze spatiotemporal variability and trends of intra-seasonal rainfall and temperature using site-specific daily data from the Ethiopian Meteorology Institute (1992–2021). Standardized methods explore variability, while Mann–Kendall tests identify trends, using the Modified version for data with autocorrelation. Inverse Distance Weighted interpolation was employed for spatial analysis of rainfall, length of growing season, and temperature. The findings identified that Kiremt dominated the mono-modal rainfall pattern, contributing 72%-86% of total annual rainfall. The study found that the season typically begins early on June 13 in Adiszemen, and July 6 in Arbgebiya and ends between October 6 and October 26. The duration of the season varied across locations, averaging 95 days at Ebenat and 148 days at Adiszemen. The seasonal rainfall anomaly index shows identical patterns between ENSO episodes and seasonal rainfall. These findings inform decision-making and adaptation strategies for ENSO-driven rainfall variability. Temperatures showed predictable seasonal patterns, but have significantly increased over time, with maximum and minimum temperatures rising 0.014 °C to 0.421 °C and 0.027 °C to 0.485 °C per year respectively. This warming trend is negatively impacting water, crops, and livestock, requiring adaptation measures to build regional resilience. This study underscores the critical impact of climate variability on agriculture in the study area. The findings reveal shifts in rainfall patterns and temperature trends, providing essential insights for adapting agricultural practices. [ABSTRACT FROM AUTHOR]

Understanding 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. [ABSTRACT FROM AUTHOR]

As more and more ancient sites are discovered around the world, protecting them in situ has become a challenge due to issues such as ground settlement and masonry wall leaks caused by groundwater fluctuation or rainfall. In this study, laboratory tests, borehole tests and field high‐density resistivity detections are conducted to obtain information for numerical modelling, including design parameters. A complex three‐dimensional hydrological–mechanical coupling model is then established to investigate ground settlement and wall deformation caused by groundwater fluctuation and rainfall. The seepage simulation results for the initial state are accurately verified by high‐density resistivity imaging. Both measured data and numerical results indicate that changes in a single water head point mainly result in wall settlement. The pattern of wall deformation changes from settlement to lateral deformation with an increase in the drawdown rate of groundwater level. Furthermore, delayed rainfall and high‐intensity rainfall can increase foundation settlement and wall deformation. Settlement deformation determines the upper limit of the global deformation when wall nodes are mainly affected. In contrast, if lateral spreading dominates wall deformation, it determines the lower limit of the global deformation. This study provides reference for in situ protection and foundation reinforcement of ancient sites. [ABSTRACT FROM AUTHOR]

In recent years, climate events such as Drought, El Niño, and La Niña have become increasingly frequent and more intense. Oceanographic monitoring was used to collect hydrological data in the middle and lower sectors of the Caeté estuary in different years. Negative rainfall anomalies of up to 45% were recorded during periods marked by drought and El Niño events, which make the water in the Caeté estuary more saline and alkaline. During these events, the retention of dissolved inorganic nutrients in the middle sector appears to support increased eutrophication and more productive waters, whereas moderate eutrophication and lower productivity were observed in the lower sector. During La Niña events, by contrast, positive rainfall anomalies may reach 60%, resulting in more oxygenated water in the estuary. In addition, the lower sector tends to be more eutrophic during periods of high rainfall and freshwater discharge, as observed in this study during a La Niña event. The paucity of data on the effects of extreme climate events in Amazonian environments means that the findings of the present study may provide a useful model for the assessment of the effects of these events on other natural environments in the Amazon region. [ABSTRACT FROM AUTHOR]

As governments continue to address climate change when formulating policy, there remains a need to determine if such a change exists in the historical record to inform clear indices for monitoring the present climate for site-specific interventions. This study characterised trends and anomalies in rainfall and dry spells, providing local information often projected from satellites or regional data in data-scarce regions. From 1961 to 2007, daily rainfall records in Central Malawi were used to calculate indices for low-(Balaka), medium-(Bunda, Chitedze, KIA), and high-altitude (Dedza) sites, which were then subjected to Mann–Kendall's, Cramer's, and Spearman-Rho's trend tests. Significant decreasing trends in terms of wet days and growing season length were evident across locations. Seasonal and extreme rainfall, dry spells, and inter-seasonal and near-decadal anomalies were not consistently or inevitably significant. Unexpectedly, rainfall anomalies were largest in Bunda and KIA, which have mild climatic regimes, while the lowest were in Balaka, a rainfall-averse zone. The relationship between El Niño-Southern Oscillation (ENSO) and extreme rainfall and dry spell events did not reach statistical significance. In conclusion, extreme precipitation and dry spell events show varied intensities and proportions rather than increased frequency. The disparate results largely justify the need for in-depth local-scale assessments for agroclimatic applications. [ABSTRACT FROM AUTHOR]

The Indian Central Himalaya Region (ICHR), the northern topographic front of the Indian summer monsoon (ISM), is an ideal location to study topography-climate interactions because two weather systems—the ISM and mid-latitude westerlies (MLW)—create distinct eco-climatic regimes from tundra (north) to tropical (south). The region's paleoclimatic studies show considerable climatic variations since the late Pleistocene. We evaluated 29 paleoclimatic records from the region and synthesized the results semi-quantitatively using the weighted palaeoclimate index (WApCI) to better understand the important climatic events and their driving mechanisms. According to the WApCI, the region has at least six enhanced monsoonal periods and eight drier spells during the past 34 ka. The cold-dry climatic events, such as the last glacial maxima (LGM), Younger Dryas (YD), 8.2 ka, and 4.2 ka, are associated with northern-hemisphere (NH) climate dynamics and propagated via MLWs. While, the warmer phases are dictated by the insolation-driven ISM dynamics. The WApCI's reconstructed rainfall anomaly aligns with paleoclimatic-model experiments for dynamically generated Paleoclimate Modeling Intercomparison Projects (PMIP3/PMIP4) rainfall for chosen time-slices (last-millenium, historical, mid-Holocene, and LGM). Finally, the Granger causality test determines the temporal relationship between the climatic drivers/forcing indices and primary meteorological parameters. The results showed that summer and post-monsoon precipitation is primarily influenced by total solar irradiation, winter precipitation is driven by a complex mix of variables, and pre-monsoon precipitation is driven by the Arctic oscillation. Based on the facts, we hypothesize that past climate variability demonstrates a complex interplay of local and hemisphere teleconnections in ICHR's climate dynamics. [ABSTRACT FROM AUTHOR]

The observed negative response of sea surface temperature (SST) to local summer rainfall peaks, with a lag of 5–10 days over the Indo-Northwest Pacific region, questions the accuracy of atmospheric models in simulating intraseasonal variability. An Atmospheric Model Intercomparison Project simulation forced by daily-varying SSTs (daily-AMIP) is used to evaluate the above question through comparison with its parallel coupled general circulation model (CGCM). In comparison to CGCM, daily-AMIP overestimates intraseasonal rainfall variability, especially over the South China Sea and the Northwest Pacific, consistent with differences in the mean state featuring a cyclonic low-level circulation pattern. Nevertheless, both CGCM and daily-AMIP demonstrate similar northward propagation of rainfall anomalies on the intraseasonal timescale, but daily-AMIP presents a larger amplitude, attributed to the absence of high-frequency ocean-atmosphere coupling and a stronger conversion of kinetic energy due to enhanced monsoon-trade winds confluence. The feedback of the ocean to the atmosphere is realized through surface heat flux exchange, and the near-quadrature relationship between intraseasonal SST and rainfall is considered evidence for ocean-atmosphere interaction. Implications for assessing the ability of AMIP to simulate intraseasonal variability over the Indo-Northwest Pacific region are discussed. [ABSTRACT FROM AUTHOR]

Spatial pattern and variability of sea surface temperature SST and their teleconnections with rainfall dynamics in the Gulf of Guinea (GOG) were examined in this study. SST and rainfall data of 50 years (1970–2022) were obtained from ERA5 and NOAA CPC at 0.25° x × 0.25° and 0.5° × 0.5° spatial resolution from longitudes 10°W and 8°W, 6oW, 4oW, and 2oW and latitudes 15°N, 5oN, 3oN, 15oS, 5oS, and 3oS, distributed along the Gulf of Guinea (GOG) respectively. Analysis was further carried out on twelve rainfall gridded stations distributed along the Gulf of Guinea (GoG) for the characterization of the Rainfall-SST teleconnection across the region while the relationship between the rainfall-SST anomalies, seasonal, inter-annual, and decadal scales was carried out using correlation analyses and composites. Interpolation of the meteorological variables was carried out using Inverse Distance Weight (IDW) from the ArcGIS Spatial Analyst Tool, Ferret, and CDO which were further employed to generate the seasonal and decadal rainfall and SST maps and statistical analysis of the study area. The result of the decadal and seasonal analysis of SST variability from 1970–1980,1980–1990, 1990–2000,2000–2010,2010–2020 and 2022 indicate that SST was highest from 2010 to 2020 at 28.91 °C and fluctuated between (28.49 °C) in the 1970–1980 and (28.08 °C) for the 1980–1990 decade While the seasonal pattern of SST showed marked variability with March–April and May(MAM) recording 29.34 °C with the lowest being in June-July–August(JJA) at 28.7 °C. In terms of decadal analysis of rainfall, the period 2010–2020 recorded the highest amount of rainfall along the coast (3,145.5 mm-3,928.3 mm while 1970–1980 recorded the lowest amount of rainfall (2,650–3.310 mm. To investigate the teleconnection between of SST and rainfall dynamics, statistical analysis was used where the SST values were plotted against seasonal rainfall in 11 stations namely Abidjan, Banjul, Accra, Guinea, Conakry, Cotonou, Dakar Doula, Freetown, Lagos, Lome, and Monrovia. The outcome of the statistical analysis and Standardized Anomaly Index used indicate that Banjul, Cotonou, Dakar, and Doula exhibited statistically insignificant correlation at 0.05 confidence level while Abidjan, Accra, Lagos, Lome, Freetown, and Monrovia showed positive and statistically significant correlation. The spatial pattern of seasonal rainfall climatology categorized into DJF, MAM, JJA, and SON reveals that JJA and SON produced 80% of rainfall in the Coastal GOG followed by MAM. The study affirmed that warm and cold tongues exist in the GOG alongside positive teleconnection and that the spatial variability of SST observed in this study corresponds positively with the decadal and seasonal variability of rainfall. [ABSTRACT FROM AUTHOR]

Despite common background La Niña conditions, Australia was very dry in November 2020 and wet in November 2021. This paper aims to provide an explanation for this difference. Large-scale drivers of Australian rainfall, including El Niño–Southern Oscillation, Indian Ocean dipole, Southern Annular Mode, and Madden–Julian oscillation, were examined but did not provide obvious clues for the differences. We found that the absence (in 2020) or presence (in 2021) of an enhanced thermal wind and subtropical jet over the Australian continent contributed to the rainfall anomalies. In general, La Niña sets up warm sea surface temperatures around northern Australia, which enhances the meridional temperature gradient over the continent and hence thermal wind and subtropical jet. In November 2021, these warm sea surface temperatures, coupled with a persistent midlatitude trough, which advected cold air over the Australian continent, led to an enhanced meridional temperature gradient and subtropical jet over Australia. The enhanced jet provided favorable conditions for the development of rain-bearing weather systems across Australia. In 2020, the continent was warm, displacing the latitude of maximum meridional temperature gradient south of the continent, resulting in fewer instances of the subtropical jet over Australia, and little development of weather systems over the continent. We highlight that although La Niña tilts the odds to wetter conditions for Australia, in any given month, variability in temperatures over the continent can contribute to subtropical jet variability and resulting rainfall in ways which confound the normal expectation from La Niña. Significance Statement: Forecasts of El Niño–Southern Oscillation are eagerly awaited, as the state of this climate driver has profound impacts on the likelihood of rainfall in regions around the world. While El Niño and La Niña do change rainfall likelihoods, the actual outcomes of these events are sometimes counter to expectation. This work explores one of the confounding factors to those expectations in the Australian context—the role of the meridional temperature gradient over the continent in modifying the storm track over Australia, which can disrupt the expected El Niño and La Niña teleconnections. We present case studies for two La Niña springs, highlighting that the Australian continent can help shape its own weather toward wetter or drier outcomes. [ABSTRACT FROM AUTHOR]

This article presents a comparative study of meteorological drought indices in the Adilabad district of Telangana, India. It emphasizes the significance of water as a crucial resource and the need to effectively manage extreme water events like droughts and floods. The researchers analyze different drought indices, such as the Standardized Precipitation Index (SPI), Decile Indicator (DI), Rainfall Anomaly Index (RAI), and Z Score Index (ZSI), to identify and monitor drought conditions. The study area, Adilabad district, is described, along with its primary industries and crops. The article provides details on the weather data used and explains the calculation methods for the various drought indices. The text discusses the calculation and comparison of these indices in the region from 1981 to 2023, identifying certain years as dry or wet, with some discrepancies between the indices. The study emphasizes the importance of using multiple indices for a comprehensive understanding of drought conditions. Overall, the drought condition in the region has remained relatively stable over the past four decades. The article acknowledges the Indian Meteorological Department for providing rainfall data and states no conflicts of interest or funding for the research. [Extracted from the article]

The presence of polycyclic aromatic hydrocarbons (PAHs) pollution on urban road surfaces is one of the major environmental concerns. However, knowledge on the distribution variability of PAHs in road dusts (RDS) and stormwater is limited, which would restrict the further risk evaluation and mitigation implementation of PAHs in road stormwater runoff. This study collected RDS samples and stormwater samples on fourteen urban roads in Shenzhen, China. This study investigated the variation of sixteen PAHs species in RDS and stormwater, and further evaluated the intrinsic and extrinsic factors which influence PAHs accumulation on urban road surfaces. The research outcomes showed significant differences on spatial distribution of PAHs in RDS and in stormwater. The land use types, industrial, commercial and port areas and vehicular volume have a positive relationship with PAHs abundance while dust particle size showed a negative correlation with PAHs abundance. For two phases in stormwater, fluctuation of PAHs with the rainfall duration in total dissolved solid (TDS) was more intensive than in dissolved liquid phase (DLP). This indicated when PAHs attached to RDS enter stormwater, most of PAHs still tend to be on solid particles than in liquid. The study outcomes are expected to contribute to efficient designs of PAHs polluted stormwater mitigation. [ABSTRACT FROM AUTHOR]

In recent years, climate events such as Drought, El Niño, and La Niña have become increasingly frequent and more intense. Oceanographic monitoring was used to collect hydrological data in the middle and lower sectors of the Caeté estuary in different years. Negative rainfall anomalies of up to 45% were recorded during periods marked by drought and El Niño events, which make the water in the Caeté estuary more saline and alkaline. During these events, the retention of dissolved inorganic nutrients in the middle sector appears to support increased eutrophication and more productive waters, whereas moderate eutrophication and lower productivity were observed in the lower sector. During La Niña events, by contrast, positive rainfall anomalies may reach 60%, resulting in more oxygenated water in the estuary. In addition, the lower sector tends to be more eutrophic during periods of high rainfall and freshwater discharge, as observed in this study during a La Niña event. The paucity of data on the effects of extreme climate events in Amazonian environments means that the findings of the present study may provide a useful model for the assessment of the effects of these events on other natural environments in the Amazon region.

The impact of the boreal summer intraseasonal oscillation (BSISO) on rainfall anomalies in the Yangtze–Huaihe River Basin (YHRB) was found to be modulated by the stratospheric quasi‐biennial oscillation (QBO), which is reasonable for the close associations between the QBO and summer extreme precipitation in the YHRB. It was found that the extreme precipitation preferentially occurred in the YHRB during the easterly phase of the QBO (EQBO) compared to the westerly phase of the QBO (WQBO). This was primarily because the BSISO was more prone to cause rainfall extremes in the YHRB during the EQBO summers than during the WQBO summers. The EQBO can induce the background stratospheric easterly wind to arch downward into the troposphere, which enhanced the BSISO‐associated moisture transport and moisture convergence and thus resulted in stronger rainfall extremes in the YHRB. Plain Language Summary: Since previous studies have demonstrated that the stratospheric quasi‐biennial oscillation (QBO) plays a critical role in rainfall variability and extremes in East Asia, in this study, we focused on the associations between the QBO and summer extreme precipitation in the Yangtze–Huaihe River Basin (YHRB). Due to QBO, the long‐standing interannual background cannot fully explain the phenomenon that rainfall extremes can hardly occur at all times under the same background. The boreal summer intraseasonal oscillation (BSISO) was considered in this study because it is one of the most critical factors that directly affects the rainfall variability and extremes in the Asian monsoon region. The impact of the BSISO on summer rainfall extremes in the YHRB is evidently modulated by the QBO. Enhanced rainfall extremes preferentially occurred in the YHRB during the easterly phase of the QBO (EQBO) compared to the westerly phase of the QBO (WQBO). Due to the EQBO backgrounds in the stratosphere, the easterly wind can arch downward into the troposphere and enhance the BSISO‐associated moisture transport and moisture convergence, contributing to the much stronger rainfall extremes in the YHRB. Key Points: QBO is closely associated with summer rainfall extremes in the Yangtze–Huaihe River Basin (YHRB)BSISO during the EQBO summers is more prone to cause rainfall extremes in the YHRB than those during WQBO summersEQBO enhances BSISO‐associated moisture transport and moisture convergence, resulting in rainfall extremes in the YHRB [ABSTRACT FROM AUTHOR]

Recent research has shown that there is a projected increase in the frequency of strong positive Indian Ocean dipole (spIOD) events in terms of both sea surface temperature (SST) and precipitation. However, it is not clear whether the spIOD events defined by SST and precipitation, hereafter referred to as SST-spIOD and Pr-spIOD events, respectively, will increase at the same pace under greenhouse warming. Using climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and CMIP6 that can reasonably simulate the spIOD events, here we find that the occurrence of Pr-spIOD events increases much more than that of SST-spIOD events (+217% vs +77%) under the future high-emission scenario. The diverse response stems from a notably large increase in the spIOD events with strong rainfall but moderate SST anomalies (Pr-only spIOD), which is facilitated by a change in the local meridional Hadley circulation induced by land–sea thermal contrast in addition to the background mean-state SST warming. For the spIOD events with prominent anomalies in both rainfall and SST (concurrent spIOD), their occurrences are projected to increase, but their average SST amplitude is projected to weaken. The increased occurrence is associated with a westward-extended structural change induced by mean linear zonal advection feedbacks, while the weakened event-average SST amplitude results from a more stabilized atmosphere and coincides with the projected reduction in IOD SST skewness. Our results suggest that IOD-related mitigation strategies should consider the diverse responses of different kinds of spIOD events to greenhouse warming. [ABSTRACT FROM AUTHOR]

In geotechnical analysis, the factor of safety (FOS) is crucial for slope stability assessment. Traditional methods often overlook the nuances of partial slope instability. Accurately determining geotechnical parameters for FOS in complex simulations is challenging and resource-intensive. The limit equilibrium method (LEM), considering unit weight, cohesion, and internal friction angle, is used to address this. This study focuses on the Jiuxianping landslide, analyzing its stability and failure behavior. Utilizing the Bayesian theorem, the study back-analyzes shear strength parameters, considering partial instability and uncertainties in the Janbu corrected method. The parameters' posterior distribution is determined using the Markov Chain Monte Carlo (MCMC) method and Multivariate Adaptive Regression Splines (MARS) for efficient sampling. These parameters are then used for precise FOS calculation at the critical point of partial instability, corroborated by 2021 data from the Jiuxianping landslide. The study finds that while the entire slope remains stable, partial instability caused by long-term water erosion significantly lowers the FOS about 10.9%, highlighting its critical impact on overall slope stability. [ABSTRACT FROM AUTHOR]

Accurate drought prediction is crucial for enhancing resilience and managing water resources. Developing robust forecasting models and understanding the variables influencing their outcomes are essential. This study developed models that integrate wavelet transformation (WT) with advanced artificial intelligence (AI) models, increasing prediction accuracy. This study investigates the prediction of meteorological droughts using standalone bootstrapped random forest (BRF) and bi-directional long short-term memory (Bi-LSTM) models, compared to wavelet-decomposed hybrid models (WBRF, WBi-LSTM). These models were evaluated in the Mun River Basin, Thailand, utilizing monthly meteorological data (1993–2022) from the Thai Meteorological Department. The predictions were assessed using statistical metrics (R2, MAE, RMSE, and MAPE). For the Standardized Precipitation Index (SPI), the hybrid WBRF model consistently outperformed the standalone BRF across various metrics and timescales, demonstrating higher R2 (0.89–0.97 for SPI-3) and lower error metrics (MAE: 0.144–0.21 for SPI-6, RMSE: 0.2–0.3 for SPI-12). Similarly, the hybrid WBi-LSTM model outperformed the standalone Bi-LSTM in SPI predictions, exhibiting higher R2 (0.87–0.91 for SPI-3) and lower error metrics (MAE: 0.19–0.23 for SPI-6, RMSE: 0.27–0.81 for SPI-12) across all timescales. This trend was also observed for the China Z-index, Modified China Z-index, Hutchinson Drought Severity Index, and Rainfall Anomaly Index, where hybrid models achieved superior performance compared to standalone models. The WBi-LSTM model emerged as the preferred choice across different timespans. The integration of WT enhanced the predictive accuracy of hybrid models, making them effective tools for drought prediction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presented an assessment of climate extremes in the Southeast Asia (SEA) region, utilizing downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The study outputs uncovered statistically significant trends indicating a rise in extreme precipitation and temperature events throughout SEA for both the near-term (2021–2060) and long-term (2061–2100) future under both SSP245 and SSP585 scenarios, in comparison to the historical period (1950–2014). Moreover, we investigated the seasonal fluctuations in rainfall and temperature distributions, accentuating the occurrence of drier dry seasons and wetter rainy seasons in particular geographic areas. The focused examination of seven prominent cities in SEA underscored the escalating frequency of extreme rainfall events and rising temperatures, heightening the urban vulnerability to urban flooding and heatwaves. This study's findings enhance our comprehension of potential climate extremes in SEA, providing valuable insights to inform climate adaptation, mitigation strategies, and natural disaster preparedness efforts within the region. [ABSTRACT FROM AUTHOR]

Mass failures are often induced or activated by the variation of hydrological factors within a reservoir area. Clarifying the relationships between the failure volume and different hydrological factors may help to identify both the dominant factors influencing reservoir bank failures and the most hazardous combinations of factors inducing such events. This study examines the influence of different hydrological factors on bank failures using model experiments incorporating variations in rainfall, wave and water level. The results indicate that the coupled effects of heavy rainfall and falling water‐level had the most significant impact on bank failure. The cumulative failure mass of the reservoir bank peaked at 177.2 × 103 cm3 under the coupled effects of rainfall and falling water‐level. In comparison, the cumulative mass failures on the reservoir bank under rainfall superimposed onto waves and rainfall superimposed onto water level rise were 162.4 × 103 and 129.2 × 103 cm3, respectively. The impact of heavy rainfall significantly contributed the mass failures on soil reservoir bank. The analysis reveals that the highest cumulative failure, 106.3 × 103 cm3, occurred during periods of heavy rainfall. Compared with heavy rainfall, the cumulative failure volumes in the reservoir experienced a reduction of 46% and 55% under solely declining and solely rising reservoir water levels, respectively. The early phases of changing hydrological conditions have a significant effect on the stability of the soil reservoir bank. Mass failures on the bank predominantly occurred during the initial three events of the five simulation experiments performed for each landform, which could account for up to 87% of the total volume. Hence, it is advisable to avoid lowering reservoir water levels during periods of sustained heavy rainfall in order to mitigate the risk of bank failure. [ABSTRACT FROM AUTHOR]

Climate change and climate variation influence the occurrence of natural disasters, especially water-related disasters. Landslides are a serious hydrogeohazard in Thailand. Hence, this study aims to project the spatial changes in the landslide susceptibility area in the Nakhon Si Thammarat province of southern Thailand. The study utilizes five climate models of CMIP6 (INM-CM4-8, IITM-ESM, MPI-ESM1-2-LR, CNRM-ESM2-1 and CNRM-CM6.-1) and conducts analyses based on 2 shared socioeconomic pathways (SSPs) of tier 1, which include SSP2-4.5 and SSP5-8.5. The linear scaling bias correction technique is applied to adjust for any biases in the climate models, and the model results are combined using the ensemble mean method. To achieve the study aims, a GIS-aided physically based landslide susceptibility model is used to predict changes in spatial aspects of the landslide susceptibility area. The analysis predicted spatial changes in the landslide susceptibility area over the next 20 years (2023 to 2042). According to the analysis, the results showed that the maximum increase in landslide susceptibility areas is approximately 29%, and the maximum decrease in landslide susceptibility areas is approximately 25% under SSP2-4.5. For SSP5-8.5, the maximum increase in landslide susceptibility areas is approximately 31%, and the maximum decrease in landslide susceptibility areas is approximately 35%. Moreover, the fluctuation of rainfall strongly influences the increase or decrease in landslide susceptibility areas. However, the landslide-prone areas remain located in areas that have experienced landslides in the past. [ABSTRACT FROM AUTHOR]

Rainfall variability and drought events can affect food security globally. This study aimed to assess the spatial and temporal trends and variability of rainfall in Kaduna State, Nigeria. Gridded rainfall data were obtained from the Climate Research Unit (CRU_TS 4.07) (1952–2022) for Birnin Gwari, Kaduna, Kafanchan and Zaria towns. The rainfall data were subjected to the Anderson‒Darling normality test and found to be normally distributed. The data were analysed using the coefficient of variation (CV), rainfall anomaly index (RAI) and precipitation concentration index (PCI). The trend was assessed using the Mann‒Kendall trend test, and finally, meteorological drought was computed using the standardized precipitation index (SPI). The results revealed that all four locations had low to moderate variability, with CVs < 15%. The rainfall anomaly index revealed that 1983 was the driest year for Birnin Gwari, Kaduna and Kafanchan, with RAI values of − 4.863, − 4.474 and − 4.316, respectively, while in Zaria, the driest year was 1974, with a RAI value of − 4.065. The PCI showed that there was a high concentration of rainfall in Birnin Gwari, Kaduna, and Kafanchan. However, Zaria experienced 41 years of high concentrations and 30 years of very high concentrations of precipitation. The Mann‒Kendall trend test revealed that all four locations experienced a significant downward trend in rainfall within the study period. The Z statistics for Birnin Gwari, Kaduna, Kafanchan and Zaria were − 1.112, − 0.357, − 0.556 and − 0.764, respectively, all indicating decreasing rainfall amounts. Finally, the SPI Birnin Gwari and Kaduna experienced 18 months of severe drought, with maximum intensities of − 2.671 and − 3.111, respectively. Kafanchan experienced 25 months of drought with a maximum intensity of − 2.643, while Zaria experienced 30 months of drought with a maximum intensity of − 3.069. These severe droughts occurred from the 1970s to the 1990s. The study concluded that rainfall concentrations were mostly high and very high, and severe, catastrophic drought events occurred in the 1970s and 1980s. [ABSTRACT FROM AUTHOR]

The present study examines long-term annual, seasonal and monthly changes and short-term fluctuations in monsoonal rainfall and temperature over plain and hill zones of Uttarakhand. Both rainfall and temperature data for period of 1981-2020 were analyzed in this study. Statistical trend analysis techniques namely Mann-Kendall test and Sen's slope estimator were used to examine and analyze the problems. The detailed analysis of the data for 40 years indicates that the annual range of temperature is increasing in the hill zone. In the context of agriculture, the observed trends in temperature and rainfall patterns pose significant implications for farming practices and crop yields in Uttarakhand. The increasing annual range of temperature, particularly in the hill zone, suggests a heightened risk of temperature extremes such as heat waves and cold spells. During the monsoon season, while rainfall levels may remain relatively stable or exhibit minor fluctuations, the variability highlighted by the box and whisker plots underscores the importance of preparedness for both excess and deficit rainfall scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Middle East Journal for Humanities & Cultural Studies is the property of Manar Elsharq for Studies & Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present research endeavors to examine the effectiveness of four gridded precipitation datasets, namely Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Precipitation Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), with the observed rainfall data of eight rain gauge stations of India Meteorological Department (IMD) from 2001 to 2019 in Kosi River basin, India. Various statistical metrics, contingency tests, trend analysis, and rainfall anomaly index were utilized at daily, monthly, seasonal, and annual time scales. The categorical metrics namely probability of detection (POD) and false alarm ratio (FAR) indicate that MERRA-2 and IMERG datasets have the highest level of concurrence with the observed daily data. Statistical analysis of gridded datasets with observed dataset of IMD showed that the performance of the IMERG dataset is better than MERRA-2, PERSIANN, and TRMM datasets with "very good" coefficient of determination (R2) and Nash–Sutcliffe Efficiency values for monthly data. Trend analysis of gridded seasonal data of IMERG showed similar trends of observed seasonal data whereas other dataset differs. IMERG also performed well in identifying wet and dry years based on annual data. Discrepancies of the satellite sensor in capturing the precipitation have also been discussed. Thus, the IMERG dataset can be effectively used for hydro-meteorological and climatological investigations in cases of lack of observed datasets. [ABSTRACT FROM AUTHOR]

The Community Earth System Model version 1 (CESM1) and version 2 (CESM2)'s abilities to simulate the impacts of Atlantic multidecadal variability (AMV) and Pacific multidecadal variability (PMV) on South American precipitation and temperature have not been assessed, and how the AMV and PMV modulate each other's influences on South American climate is not well understood. Here we use observations, reanalyses, and CESM1 and CESM2 simulations from 1920 to 2015 to study those problems. The models can reproduce the observed precipitation and temperature responses to AMV well, but can only roughly reproduce such responses to PMV. The precipitation response over the South Atlantic convergence zone (SACZ) is better simulated by CESM2 compared to CESM1, which is associated with an improved horizontal moisture flux over this region. However, the models cannot accurately simulate the observed differences between the influences of Pacific interannual and multidecadal variability on South American precipitation and temperature. The impacts of AMV and PMV on South American precipitation are modulated by the other mode via changes in horizontal moisture flux over the SACZ and River Plate basin in summer, as well as changes in vertical motion over the equatorial regions in winter. Similarly, the impacts of AMV and PMV on South American temperature are also modulated by the other mode. Over water‐limited regions, such as northeastern Brazil and southern Argentina, the precipitation and temperature responses are anti‐correlated, possibly via surface evaporation. Plain Language Summary: How the Community Earth System Model version 1 and version 2 simulate the influences of Atlantic and Pacific multidecadal sea surface temperature variations on South American climate has not previously been examined, and how the Atlantic and Pacific multidecadal variability (AMV and PMV) modulates the effects from the other basin on South American precipitation and temperature is unclear. In this study, we analyze historical observations and model simulations during 1920–2015 to investigate those problems. The general patterns of the precipitation and temperature responses to AMV are well simulated by both versions of the model, but the patterns of the responses to PMV are only roughly reproduced by the models. The precipitation response over the South American monsoon region is better represented in the model's version 2 than in version 1. The Atlantic and Pacific variability modulates each other's impacts on South American precipitation and temperature. The variations in the patterns of precipitation response are related to changes in horizontal moisture transport over subtropical regions in summer and vertical motion over equatorial regions in winter. The precipitation and temperature responses are anti‐correlated over relatively dry regions, such as northeastern Brazil and southern Argentina, possibly through surface evaporation. Key Points: The Community Earth System Model version 2 improves monsoon rainfall response to Pacific and Atlantic variability compared to version 1Precipitation anomaly patterns over South America are related to anomalous subtropical moisture flux and equatorial vertical motionOver water‐limited regions, anomalies in temperature and precipitation are anti‐correlated, possibly due to surface evaporative cooling [ABSTRACT FROM AUTHOR]

Understanding how models represent sub-seasonal rainfall variations and what influences model skill is essential for improving sub-seasonal forecasts and their applications. Here, empirical orthogonal function (EOF) analysis is employed to investigate weekly Eastern Africa short rains variability from October to December. The observed leading EOF modes are identified as (i) a monopole-like rainfall pattern with anomalies impacting southern Ethiopia, Kenya, and northern Tanzania; and (ii) a dipole-like rainfall pattern with contrasting anomalies between Tanzania and the northeastern sector of Eastern Africa. An examination of the links between the leading modes and specific climate drivers, namely, the Madden–Julian Oscillation (MJO), El Niño–Southern Oscillation, and Indian Ocean Dipole (IOD), shows that the MJO and IOD have the highest correlations with the two rainfall modes and indicates that the monopole (dipole)-like rainfall pattern is associated with MJO convective anomalies in the tropical Indian Ocean and western Pacific (Maritime Continent and Western Hemisphere). Assessments of model ability to capture and predict the leading modes show that the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Met Office models outperform the National Centers for Environmental Prediction model at forecast horizons from one to four weeks ahead. Amongst the drivers examined, the MJO has the largest impact on the forecast skill of rainfall modes within the ECMWF model. If MJO-related variability is reliably represented, the ECMWF model is more skilful at predicting the main modes of weekly rainfall variability over the region. Our findings can support model developments and enhance anticipatory planning efforts in several sectors, such as agriculture, food security, and energy. [ABSTRACT FROM AUTHOR]

Interaction between rainfall and groundwater is a complex phenomenon as it varies spatially with respect to local hydrogeological characteristics and temporally with hydro meteorological variations. To understand the resilience of the groundwater system to the changing climate, rainfall-groundwater interaction needs to be analysed in detail. The Rainfall and Time Trends (HARTT) have been widely used in recent studies to model the impact of the rainfall and the groundwater level fluctuation. However, not many such studies have been reported in tropical hilly areas. Bhadra reservoir catchment is a tropical hilly catchment in India which is characterised by heavy rainfall, steep topography and shallow water table conditions and significant subsurface flow conditions. The area is largely dependent on groundwater for irrigation and drinking water supply. Nonparametric statistical methods such as Mann–Kendall (MK) test, Sen's slope estimator and Sequential Mann–Kendall Test (SQMK) are used to study the temporal trends in the rainfall and static groundwater levels. Further, contribution of rainfall to groundwater level fluctuation is analysed using HARTT model. Study shows a decreasing trend in rainfall and groundwater level in the upper catchment. Further, 1 mm increase or decrease in the rainfall anomaly is estimated to cause 0.0012 m decrease or increase in the depth to water table. Only 36% of the groundwater level fluctuation is explained by rainfall induced recharge; nevertheless, the relation is non-uniform throughout the catchment. Spatial variation in topography and hydrogeological conditions is suspected to have significant impact on the rainfall and groundwater interaction leading to poor coefficient of determination for the regression model between rainfall anomaly and the groundwater level fluctuations. [ABSTRACT FROM AUTHOR]

The drought is a prominent disaster the world is facing currently. The drought identification, its characterization, and monitoring are, thus, very important to mitigate this calamity. Many drought indices have been developed for this purpose in the past; however, their evaluation is necessary to select the best suited drought index for the specific area. This study aims to analyze the meteorological drought risk across the Raigarh district of Chhattisgarh state by evaluating five popular drought indices, namely, Reconnaissance drought index (RDI) rainfall anomaly index, standardized precipitation evapotranspiration index (SPEI), standardized precipitation index (SPI), and standardized anomaly index (SAI) considering all the tehsils of the district and proposes the methodology to select the best suited index for the region. The methodology has been selected in such a manner that it deals with all aspects of meteorological drought and the most suitable drought index for the region can be identified by comparing the drought indices based on the actual drought occurrence in the region which are identified by different agencies. These drought indices were computed for different assessment periods of 1, 3, 6, 9, and 12 months using the historical precipitation and temperature data derived from (NASA-POWER) for the period 1981–2021. The variation in severity in terms of extreme, severe, and moderate drought events has been observed with these drought indices in the district. Maximum correlation is observed between RAI and SAI across all assessment periods. Further, the SPI is seen to have fairly good agreement with all other drought indices and it rises with increasing assessment periods. Modified Mann–Kendall test shows a significant positive trend for all drought indices (except SPEI across all assessment periods, RDI-1 and RDI-3) across all assessment periods. The SPI performs exceptionally well in capturing the actual drought conditions reported by different agencies in the Raigarh region among all other indices considered in this study. It is also observed that although SPEI which comes out to be the second most suitable index for drought identification in the study region, it was not able to capture the drought events before 1990. However, SPEI identified most of the drought years post 1990 which shows the prevalent effect of temperature on the formulation of index driven by heavy industrialization in the region post 1990. Overall results suggest that the SPI is the best-suited meteorological drought index for the Raigarh region. [ABSTRACT FROM AUTHOR]

Global food security basically depends on potential yields of staple grain crops worldwide, especially under climate change. However, most scholars use various models of production function in which climatic factors are often considered to estimate crop yield mostly at local or regional level. Therefore, in this paper: Potential yields of rice, wheat, maize and soybean worldwide by 2030 are projected creatively using Auto-regressive Integrated Moving Average and Trend Regressed (ARIMA-TR) model in which actual yields in recent two years are used for testing the reliability of projection and Gray System (GS) model for validating the test; Especially individual impacts of climate change on the productions of rice, wheat, maize and soybean worldwide since 1961 are analyzed by using unary regression model in which global mean temperature and land precipitation are independent variable while the yield of crop being dependent one, respectively. Results show that: by 2030, the ratio between average and top yields of world rice is projected to be 50.6% increasing, while those of world wheat, world maize and world soybean are projected to be 38.0% increasing, 14.7% decreasing and 72.5% increasing, respectively. Since 1961 global warming has exerted a negative impact on average yield of world rice less than on its top, a positive effect on average yield of world wheat while a negative impact on its top, a positive effect on average yield of world maize less than on its top, and a positive influence on average yield of world soybean while a negative one on its top, which might be slightly mitigated by 'Carbon Peak' target. The fluctuation of global rainfall contributes to the productions of these crops much less than global warming during same period. Our findings indicate that: to improve global production of four staple grain crops by 2030, the priorities of input should be given to either rice or wheat in both high and low yield countries, whereas to maize in high yield countries and to soybean in low yield countries. These insights highlight some difference from previous studies, and provide academia with innovative comprehension and policy-decision makers with supportive information on sustainable production of these four staple grain crops for global food security under climate change in the future. [ABSTRACT FROM AUTHOR]

Growing urban population and the distinct strategies to accommodate them lead to diverse urban development patterns worldwide. While local evidence suggests the presence of urban signatures in rainfall anomalies, there is limited understanding of how rainfall responds to divergent urban development patterns worldwide. Here we unveil a divergence in the exposure to extreme rainfall for 1790 inland cities globally, attributable to their respective urban development patterns. Cities that experience compact development tend to witness larger increases in extreme rainfall frequency over downtown than their rural surroundings, while the anomalies in extreme rainfall frequency diminish for cities with dispersed development. Convection-permitting simulations further suggest compact urban footprints lead to more pronounced urban-rural thermal contrasts and aerodynamic disturbances. This is directly responsible for the divergent rainfall responses to urban development patterns. Our analyses offer significant insights pertaining to the priorities and potential of city-level efforts to mitigate the emerging climate-related hazards, particularly for countries experiencing rapid urbanization. Cities that experience compact development tend to witness more extreme rainfall over downtown than their rural surroundings, while the anomalies in extreme rainfall frequency diminish for cities with dispersed development patterns. [ABSTRACT FROM AUTHOR]

We investigated the influence of the El Niño‐Southern Oscillation (ENSO) on inter‐annual precipitation variability in the far‐eastern Pacific (FEP) and northern South America (NSA) using an approach based on phase synchronization (PS). First, we carried out a detailed analysis of observational data to define the inter‐annual variability, eliminate the seasonal residual frequencies in hydro‐climatic anomalies, and assess the statistical significance of PS. Additionally, we characterized the seasonality of regional patterns of sea surface temperature, surface pressure levels, low‐level winds and precipitation anomalies associated with the ENSO states. We found that the positive (negative) precipitation anomalies experienced in the FEP and NSA differ from those previously reported in the literature. In particular, the Guianas (northeastern Amazon) and the Caribbean constitute two regions with negative (positive) rainfall anomalies during El Niño (La Niña), separated by a zone of non‐significant anomalies along the Orinoco Low‐level Jet corridor. Moreover, we showed that the ENSO signal is phase‐locked with inter‐annual rainfall and low‐level wind variability in most of the study regions. Furthermore, we found consistency in the PS between the Central and Eastern Pacific El Niño indices and hydroclimatic anomalies over the Pacific. However, some areas exhibited PS, although they did not show significant precipitation anomalies, suggesting that the influence of ENSO on tropical climatology manifests not only in terms of the magnitude of anomalies but also in terms of the phases only. Our approach advances the understanding of climatic anomalies in tropical regions and provides new insights into the non‐linear interactions between ENSO and hydroclimatic processes in tropical Americas. [ABSTRACT FROM AUTHOR]

Accurate and in-depth rainfall studies are crucial for understanding and assessing precipitation events' patterns, intensities, and impacts, enabling effective planning and management of water resources, agriculture, and disaster preparedness. Despite many rainfall studies in Bangladesh at the national and regional scales, study on the spatiotemporal rainfall variability is still rare at the local scale. The current study aims to apply Mann–Kendall (MK), Modified Mann–Kendall (MMK), and Innovative Trend Analysis (ITA) techniques to assess the long-term annual and seasonal rainfall trends and variability over the southeast region of Bangladesh. Monthly rainfall data from ten Bangladesh Meteorological Department climate stations between 1981 and 2022 was used for the analysis on annual and four seasonal scales. The precipitation concentration index results showed significant variations in annual rainfall across the study area, whereas seasonal PCIs were consistent with moderate rainfall. According to standardized rainfall anomaly findings, each station experienced at least one severe to extremely severe drought episode during the 42-year study period. Homogeneity tests revealed significant breakpoints in some rainfall datasets, while 78% were declared homogeneous. MK, MMK, and ITA techniques revealed similar increasing and decreasing trend patterns throughout the study area. Annual rainfall showed an upward trend in the coastal part and a downward trend in the northern part of the study area, with monsoon rainfall exhibiting a similar trend pattern. The ITA technique outperformed the MK and MMK techniques in detecting trends, identifying significant increasing and decreasing trends in 76% (38 out of 50) of the observations, while the MK and MMK techniques detected trends in only 8% and 44% of the total observations, respectively. The outcome of the current study is expected to be helpful for the sustainable planning and management of water resources in the southeast region of Bangladesh. [ABSTRACT FROM AUTHOR]

Most previous studies have investigated East Asian summer rainfall as one element. This study investigates the East Asian summer monsoon (EASM), Western North Pacific (WNP) tropical cyclones (TCs), and the concurrent rainfall occurring between May and September from 1983 to 2021. Three distinct types of rainfall are identified: monsoon-only rainfall, TC-only rainfall, and monsoon-TC joint rainfall (MS-TC rainfall), each exhibiting its own unique characteristics. Monsoon-only rainfall is characterized by positive anomalies along the East Asian subtropical front, while a decrease in TC activity leads to negative rainfall anomalies in the tropical WNP. TC-only rainfall, on the other hand, contributes to positive rainfall anomalies in the tropical WNP, as more TCs exhibit westward movement. In the case of MS-TC rainfall, positive precipitation anomalies are observed in the Philippines, the Korean Peninsula, and southern Japan. These anomalies can be attributed to an enhanced northward movement of TCs during such rainfall events. During strong monsoon-only (TC-only) rainfall years, the tropical western Pacific experiences anticyclonic (cyclonic) anomalies, along with a westward (eastward) shift of monsoon trough and WNP subtropical high (WNPSH). In strong MS-TC years, a localized cyclonic anomaly dominates the Philippine Basin, resulting in an eastward shift of the monsoon trough and WNPSH. Additionally, a significant increase (decrease) in vertical wind shear (VWS) is observed in the tropical WNP during strong monsoon-only (TC-only) years, while a moderate decrease is observed in strong MS-TC years. Distinct influential factors are associated with each rainfall type. Preceding positive sea surface temperature (SST) anomalies in the offshore China seas and WNP, in conjunction with the El Niño-Southern Oscillation (ENSO), play a primary role in driving interannual variations of monsoon-only rainfall. ENSO serves as the principal modulator of interannual variations in TC-only rainfall. Additionally, anomalous thermal conditions in the Maritime Continent (MC) act as major drivers for MS-TC rainfall. This study enhances our understanding of the underlying mechanisms and influential factors contributing to the diverse patterns of East Asian summer rainfall. [ABSTRACT FROM AUTHOR]

The study employs a hybrid Employing a hybrid ensemble empirical mode decomposition-temporal convolutional network (EEMD-TCN) machine learning approach to predict the IOD index and sea surface temperatures for various time spans (3, 6, 9, and 12 months ahead), and explores changes in the relationship between IOD and Indian summer monsoon rainfall (ISMR) across India from 1960 to 2020, considering risk and uncertainty. The methodology involves decomposing the IOD index and SST into consistent subcomponents using EEMD, followed by TCN modeling to forecast these subcomponents, resulting in predictions affected by risk and uncertainty. The IOD index's forecast precision surpasses that of SSTs, attributed to the higher number of high-frequency elements in the SST data, posing a greater challenge in prediction risk and uncertainty. The research also examines anomalies in rainfall, SST, and low-level wind circulation, highlighting the impact of IOD events. ISMR across India shows sensitivity to IOD events, with positive IOD (pIOD) events linked to increased rainfall and negative IOD (nIOD) events to decreased rainfall, except during the initial phase of the Indian summer monsoon, emphasizing the role of risk and uncertainty. Variations in SST, wind circulation, and moisture transport mechanisms in the Indian Ocean lead to notable precipitation changes during different IOD stages, especially from 1991 to 2020. Notably, a recent rise in the frequency of low-level equatorial jets (LEJs) in the Indian Ocean equatorial region and the Arabian Sea during pIOD events is observed, in contrast to the earlier decades of 1960–1990. [ABSTRACT FROM AUTHOR]

The East Asian jet stream is an important component of mid-high latitude circulation systems, and its intraseasonal oscillation plays a crucial role in the formation and development of persistent climate anomalies and extreme events over East Asia–Pacific region. This study examines the decadal changes in the intraseasonal variability (ISV) of the East Asian jet stream and investigates the possible reasons with reanalysis and observational datasets. It is found that the zonal winds over East Asia show notable ISV with a significant period of 10–25 days. The variance of 10–25-day filtered East Asian zonal winds exhibits a significant decrease around 2000, which is mainly contributed by the weakened ISV of westerlies within the key region of East Asian polar front jet (EAPJ). Further analysis reveals that the EAPJ experienced remarkable ISV changes in both intensity and location. The ISV of EAPJ intensity was weakened around 2000 due to decreased frequencies of both the extremely strong and weak cases, whereas the ISV of EAPJ location was evidently enhanced, with the southward shifts increased more than the northward ones. Along with the ISV increase (decrease) of EAPJ intensity (location), the dominant mode of EAPJ intraseasonal variation has changed from its intensity change to position shift since the year 2000. The decadal ISV changes and mode transition of EAPJ are closely associated with the modulation of lower-level large-scale circulation over Eurasia. The weakened ISV of meridional winds over Ural region after 2000 is favorable for an intraseasonal circulation pattern change from a continental-scale cyclonic anomalous pattern to a meridional dipole structure across the Eurasia in lower troposphere, resulting in regime shifts of East Asian temperature and its meridional gradient through temperature advection. The intraseasonal temperature gradient patterns display ISV changes parallel to those of EAPJ around 2000, indicating that the thermal anomalies are effective in leading to corresponding intensity and location variations of EAPJ via thermal wind relationship. Furthermore, the modulation of lower-level circulation is closely related to strengthened Arctic warming and weakened temperature variability over Barents sector after 2000, which may further be linked to sea ice reduction in the Barents Sea. [ABSTRACT FROM AUTHOR]

The northwestern Peruvian Amazon (NWPA) basin (78.4–75.8° W, 7.9–5.4° S) is an important region for coffee and rice production in Peru. Currently, no prediction models are available for estimating rainfall in advance during the wet season (January–February–March, JFM). Hence, we developed multiple linear regression (MLR) models using predictors derived from sea surface temperature (SST) indices of the Pacific, Atlantic, and Indian Oceans, including central El Niño (C), eastern El Niño (E), tropical South Atlantic (tSATL), tropical North Atlantic (tNATL), extratropical North Atlantic (eNATL), and Indian Ocean basin-wide with E and C removed (IOBW*) indices. Additionally, we utilized large-scale convection indices, namely, the eastern Pacific intertropical convergence zone (ITCZe) and South American Monsoon System (SAMSi) indices, for the 1981–2018 period. Rainfall in the lowland NWPA exhibits a bimodal annual cycle, whereas rainfall in the highland NWPA exhibits a unimodal annual cycle. The MLR model can be used to accurately capture the interannual variability during the wet season in the highland NWPA by utilizing predictors derived from the C and SAMSi indices. In contrast, regarding rainfall in the lowland NWPA, the Pacific SST variability, SAMS and tropical North Atlantic index were relevant. For long lead times, the MLR model provided reliable forecasts of JFM rainfall anomalies in the highlands (R3, approximately 2700 m asl) as these regions are governed by Pacific variability. However, the MLR model exhibited limitations in accurately estimating the wettest JFM season in the highlands due to the absence of a predictor for the amplified effect of the Madden–Julian Oscillation on rainfall. [ABSTRACT FROM AUTHOR]

Background: Rainfall-induced coastal runoff represents an important environmental impact in near-shore coral reefs that may affect coral-associated bacterial microbiomes. Shifts in microbiome community composition and function can stress corals and ultimately cause mortality and reef declines. Impacts of environmental stress may be site specific and differ between coral microbiome compartments (e.g., tissue versus mucus). Coastal runoff and associated water pollution represent a major stressor for near-shore reef-ecosystems in Guam, Micronesia. Methods: Acropora pulchra colonies growing on the West Hagåtña reef flat in Guam were sampled over a period of 8 months spanning the 2021 wet and dry seasons. To examine bacterial microbiome diversity and composition, samples of A. pulchra tissue and mucus were collected during late April, early July, late September, and at the end of December. Samples were collected from populations in two different habitat zones, near the reef crest (farshore) and close to shore (nearshore). Seawater samples were collected during the same time period to evaluate microbiome dynamics of the waters surrounding coral colonies. Tissue, mucus, and seawater microbiomes were characterized using 16S DNA metabarcoding in conjunction with Illumina sequencing. In addition, water samples were collected to determine fecal indicator bacteria (FIB) concentrations as an indicator of water pollution. Water temperatures were recorded using data loggers and precipitation data obtained from a nearby rain gauge. The correlation structure of environmental parameters (temperature and rainfall), FIB concentrations, and A. pulchra microbiome diversity was evaluated using a structural equation model. Beta diversity analyses were used to investigate spatio-temporal trends of microbiome composition. Results: Acropora pulchra microbiome diversity differed between tissues and mucus, with mucus microbiome diversity being similar to the surrounding seawater. Rainfall and associated fluctuations of FIB concentrations were correlated with changes in tissue and mucus microbiomes, indicating their role as drivers of A. pulchra microbiome diversity. A. pulchra tissue microbiome composition remained relatively stable throughout dry and wet seasons; tissues were dominated by Endozoicomonadaceae, coral endosymbionts and putative indicators of coral health. In nearshore A. pulchra tissue microbiomes, Simkaniaceae, putative obligate coral endosymbionts, were more abundant than in A. pulchra colonies growing near the reef crest (farshore). A. pulchra mucus microbiomes were more diverse during the wet season than the dry season, a distinction that was also associated with drastic shifts in microbiome composition. This study highlights the seasonal dynamics of coral microbiomes and demonstrates that microbiome diversity and composition may differ between coral tissues and the surface mucus layer. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) over the Bay of Bengal (BOB) can interact with the South Branch Trough (SBT) as they move northward and potentially amplify Rossby waves. This study evaluates the features of Rossby waves and their downstream impact on rainfall in South China. Results indicate that TC‐SBT interactions primarily occur in May and October‐November (Oct‐Nov), with probabilities of 59% and 53% respectively. Notably, the Rossby wave train associated with BOB TCs is more pronounced during Oct‐Nov due to the stronger subtropical westerly jet, in contrast to May. The downstream atmospheric response results in positive (negative) rainfall anomalies over South China in May (Oct‐Nov), particularly on the day following the maximum interaction day. Previous researches concerning TC‐extratropical flow interaction mainly focus on other basins where TCs move to higher latitudes, this study provides fresh insights into Rossby waves related to TC‐SBT interactions over the southern Tibetan Plateau. Plain Language Summary: During bimodal periods of tropical cyclone (TC) activity over the Bay of Bengal (BOB) in May and October‐November (Oct‐Nov), the Asian subtropical westerly jet is usually located at lower latitudes over the southern Tibetan Plateau, where South Branch Trough (SBT) is active. We quantified the extent of BOB TC‐SBT interaction based on the negative potential vorticity advection by TC‐associated irrotational wind. The mean location of the TC center and maximum interaction point are around (20°N, 88°E) and (33°N, 89°E), respectively. Rossby wave trains (RWT) associated with BOB TCs may extend eastward to 150°W, dispersing at a faster zonal group speed in May compared to Oct‐Nov. In May, the downstream response in South China featured by amplified upper‐level divergent outflow, intensified mid‐level warm and cold air convergence, and enhanced low‐level southwesterly water vapor transport, which was conducive to precipitation. In Oct‐Nov, the RWT shifted northward and the anticyclone anomaly related to the subtropical high dominated South China, leading to a northward shift in water vapor transport, unfavorable for precipitation in South China. The results are significant for the short‐ to medium‐range weather forecasts in downstream regions of BOB TCs. Key Points: Tropical cyclones (TC) over the Bay of Bengal (BOB) interacting with the South Branch Trough (SBT) can amplify mid‐latitude Rossby wavesThe Rossby wave associated with interactions between BOB TCs and SBTs is stronger in October‐November than in MayThe atmospheric response results in positive (negative) precipitation anomalies over South China in May (October‐November) [ABSTRACT FROM AUTHOR]

The Jinhua–Quzhou basin in China is one of the most susceptible areas to drought. Due to the loss of vegetation and great fluctuations in rainfall and surface temperature, global warming occurs. Timely, accurate, and effective drought monitoring is crucial for protecting local vegetation and determining which vegetation is most vulnerable to increased LST during the period 1982–2019. It assumes a strong correlation between loss of vegetation cover, changes in monsoon climate, drought, and increases in land surface temperature (LST). Due to significantly increased in LST, low precipitation and vegetation cover, NDVI, TVDI, VCI, and NAP are useful in characterizing drought mitigation strategies. The temperature vegetation drought index (TVDI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and monthly precipitation anomaly percentage (NAP) can be helped to characterize drought reduction strategies. Monthly NDVI, NAP, VCI, TVDI, normalized vegetation supply water index (NVSWI), temperature condition index (TCI), vegetation health index (VHI), and heat map analysis indicate that the Jinhua–Quzhou basin experienced drought during 1984, 1993, 2000, and 2011. Seasonal SR, WVP, WS, NDVI, VCI, and NAP charts confirm that the Jinhua–Quzhou basin was affected by severe drought in 1984, which continued and led to severe droughts in 1993, 2000, and 2011. Regression analysis showed a significant positive correlation between NDVI, TVDI, VCI, and NAP values, while NVSWI, TVDI, and VHI showed positive signs of good drought monitoring strategies. The research results confirm the correlation between loss of vegetation cover and LST, which is one of the causes of global warming. The distribution of drought changed a trend indicating that compared with the Jinhua region; the Quzhou region has more droughts. The changing trend of drought has characteristics from 1982 to 2019, and there are significant differences in drought changing trends between different Jinhua–Quzhou basin areas. Overall, from 1982 to 2019, the frequency of drought showed a downward trend. We believe that these results will provide useful tools for drought management plans and play a relevant role in mitigating the effects of drought and protecting humanity from climate hazards. [ABSTRACT FROM AUTHOR]

Above‐normal all‐India summer monsoon (ISM) rainfall was received in 2020 with 109% of its long‐period average. According to the India Meteorological Department (IMD), ISM 2020 experienced robust month‐to‐month rainfall variations; in particular, the peak monsoon month of July 2020 witnessed deficit rainfall. The present study investigated the possible mechanisms that are responsible for the July 2020 deficit rainfall. Analysis revealed that the strong low‐level moisture divergence caused by the westward‐propagating atmospheric Rossby wave, induced by suppressed western North Pacific (WNP) convection, is primarily responsible for the July rainfall deficit. The WNP suppressed convection is linked to anomalous low‐level anticyclonic circulation. It is noted that the intense WNP anticyclone is maintained by the tropical Indian Ocean (TIO) warming‐induced atmospheric Kelvin waves and strong low‐level convergence over the Meiyu‐Baiu rainband. Unlike July, the strength of the WNP anticyclone and TIO warming are weaker in June and August. In addition to the observational analysis, the skill of the Climate Forecast System version‐2 (CFSv2) model in predicting the July rainfall at a lead of about two months is examined. Strong positive rainfall anomalies over India are predicted by the model in July, which is in contrast to the observations. Despite predicting the TIO warming, the model failed to capture the westward extension of the WNP anticyclone, which impacted the July rainfall prediction. To understand the importance of the WNP anticyclone on ISM rainfall, a CFSv2 sensitivity experiment is carried out by imposing strong negative sea surface temperature (SST) anomalies over the WNP region to excite the anticyclone. Both the westward extension of the WNP anticyclone and suppressed rainfall over the core monsoon region in July are captured by the model when low‐level circulation is imposed. The model sensitivity experiment strongly supports the role of the WNP anticyclone and its westward extension in inducing reduced rainfall in July 2020 over the monsoon trough region. [ABSTRACT FROM AUTHOR]

Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source of local employment and income. In recent years, African studies have documented an overall decline in the extent of small-scale crop farming, with many crop fields left abandoned. This study uses rural areas in three former apartheid homelands in South Africa as a case study to quantify patterns and trends in the overall land cover change and small-scale agricultural lands related to changes in climate over a 38-year period. Random forest classification was applied on the Landsat imagery to detect land use and land cover change, achieving an overall accuracy of above 80%. Rainfall and temperature anomalies, as well as the Standardized Precipitation Evapotranspiration Index (SPEI) were used as climate proxies to assess the influence of climate variability on crop farming, as the systems investigated rely completely on rainfall. Agricultural land declined from 107.5 km2 to 49.5 km2 in Umhlabuyalingana; 54 km2 to 1.6 km2 in Joe Morolong; and 254.6 km2 to 7.4 km2 in Mangaung between 1984 and 2022. Declines in cropland cover, precipitation, and the SPEI were highly correlated. We argue that climatic variability influences crop farming activities; however, this could be one factor in a suite of drivers that interact together to influence the cropping practices in rural areas. [ABSTRACT FROM AUTHOR]

Floods in the Pampean Region have a detrimental direct effect on plant production through crop flooding and soil salinization. Understanding the hydrological drivers of landscape flooding contributes to the implementation of more efficient mitigation actions. In this study, we determine the spatio-temporal dynamics of the surfaces water (SW) in the central portion of the Inner Pampean subregion and its relationship with regional rainfall records during the last 33 years (1987 to 2019). SW was estimated from spectral indices derived from satellite images (Landsat and MODIS) with a target temporal frequency of 6 months. The rainfall regime was characterized by monthly precipitation maps generated by interpolation of records from regional rain gauges. Two drainage basins were differentiated in the study region through a topographic analysis. They were designated as north basin (2.1 Mha), that includes the main channel of the Quinto River, and south basin (6.7 Mha). We found that flood periods (i.e., SW anomalies in the percentile range of percentiles [100:<90]) were short (-2 years) and recurrent. There were two flooding events in both basins (2001/02 and 2016/17) and an additional event in the south basin (1987/88). In both basins, the temporal dynamics of the SW was poorly associated with short (3 month) or long (3 years) term accumulated rainfall water. Although all flood events corresponded to positive rainfall anomalies, not all positive rainfall anomalies corresponded with large SW. The observed low predictive power of rainfall constitutes an indication that SW anomalies are also coupled to incoming regional surface and groundwater. Therefore, flood mitigation strategies should primarily aim at deepening groundwater levels by maintaining high evapotranspiration rates. [ABSTRACT FROM AUTHOR]