Seasonal Time Research Articles

Understanding the role of temperature in seasonal timing: Effects on behavioural, physiological and molecular phenotypes

AbstractOrganisms adapt to daily and seasonal environmental changes to maximise their metabolic and reproductive fitness. For seasonally breeding animals, photoperiod is considered the most robust cue to drive these changes. It, however, does not explain the interannual variations in different seasonal phenotypes. Several studies have repeatedly shown the influence of ambient temperature on the timing of different seasonal physiologies including the timing of migration, reproduction and its associated behaviours, etc. In the present review, we have discussed the effects of changes in ambient temperature on different seasonal events in endotherms with a focus on migratory birds as they have evolved to draw benefits from distinct but largely predictable seasonal patterns of natural resources. We have further discussed the physiological and molecular mechanisms by which temperature affects seasonal timings. The primary brain area involved in detecting temperature changes is the hypothalamic preoptic area. This area receives thermal inputs via sensory neurons in the peripheral ganglia that measure changes in thermoregulatory tissues such as the skin and spinal cord. For the input signals, several thermal sensory TRP (transient receptor potential ion channels) channels have been identified across different classes of vertebrates. These channels are activated at specific thermal ranges. Once perceived, this information should activate an effector function. However, the link between temperature sensation and the effector pathways is not properly understood yet. Here, we have summarised the available information that may help us understand how temperature information is translated into seasonal timing.

Analysis of the Broiler Chicken Dead-on-Arrival (DOA) Rate in Relation to Normal Transport Conditions in Practice in Germany.

In total, around 631 million broilers were slaughtered in Germany in 2022. This evaluation included data of approx. 198 million broilers of different ages and breeds that were transported in Germany in 2022 (31% of all cases of broiler chicken transport in 2022). The aim of this study was to analyze German broiler chicken transport (n = 14,054) to the slaughterhouse between January 2022 and May 2023 with regard to the dead-on-arrival (DOA) rate and the possible influencing factors. Therefore, the relation between the total amount of animals per transport, the duration and distance of the transport, the planned stocking density in the transport cages, the average daily temperature and time of day and season of the transport as well as the DOA rate were statistically evaluated. The results showed a mean DOA rate of 0.09% (SD 0.09). Transport conducted at midday (11:00 to 17:00) showed higher DOA rates (p < 0.05) than transport at other times (day split into 6 h intervals). The highest mean DOA rate (0.10%) was found in the fall, followed by the winter, while transport in the spring and summer resulted in the lowest DOA rate (p < 0.05). All in all, the relatively low DOA rate (%) in Germany indicates the good standard of their broiler transport compared to available data from research in other European countries.

Advanced precipitation enhances vegetation primary productivity in Central Asia

Climate change is reshaping the seasonal distribution of precipitation patterns across Central Asia, affecting not only the total accumulation but also the timing of precipitation events. However, how seasonal precipitation timing (i.e., precipitation seasonality) shift and may influence dryland ecosystem responses have rarely been considered. Here we utilized gross primary productivity (GPP) data derived from satellite observations over 2001–2022 and a proxy of precipitation timing (precipitation center time index, PCT) to elucidate changes in precipitation timing and assess its impact on vegetation productivity along bioclimate gradients in Central Asia. Our findings revealed that, from southwest to northeast, the precipitation season gradually extended from spring (PCT ≤ 5.0) to summer (PCT ≥ 6.0). Notably, approximately 74.4 % of the study area exhibited a trend of advanced precipitation, particularly evident in regions characterized by earlier precipitation onset. Changes in precipitation timing were found to induce variations in vegetation productivity, albeit with spatial heterogeneity. About 82.0 % of the vegetated areas showed positive GPP anomalies with advanced precipitation, especially in the eastern drylands, while delayed precipitation depressed productivity, leading to negative GPP anomalies in nearly 65.9 % of the vegetation zones. Vegetation in arid regions demonstrated heightened susceptibility to altered precipitation timing, whereas this sensitivity diminished in humid regions characterized by greater moisture availability. More precipitation distributed in early growing season would be more favorable to increase GPP, especially in xeric sparse vegetation, grassland, rained cropland, and shrubland. Our study highlight that it is necessary to pay more attention to the effects of altered precipitation timing on changes in terrestrial ecosystem productivity related to carbon cycle, especially in dryland.

Test inferences and link function selection in dynamic beta modeling of seasonal hydro-environmental time series with temporary abnormal regimes

We consider the modeling and forecasting of hydro-environmental time series subject to seasonal fluctuations and prolonged droughts. Abnormally dry periods have become more frequent as a result of climate change. We use a class of dynamic beta models which is tailored for doubly-bounded data. We examine two important aspects of this class of models: the accuracy of hypothesis tests based on asymptotic approximations and the choice of link function. In particular, we show that two commonly used tests can yield inaccurate inferences if the estimation of the null model is done on the basis of the maximum number of individual conditional log-likelihoods, and we introduce a new model selection criterion for selecting the model’s link function. Based on tests and the new criterion, we model the useful volumes of three Brazilian hydroelectric power plant water reservoirs. These time series exhibit seasonal fluctuations and contain abnormally dry periods due to intense and prolonged droughts. In-sample predictions and out-of-sample forecasts are produced and compared to those obtained with well known alternative approaches.

Global Stratospheric Properties of Gravity Waves From 1 Year of Radio Occultations

AbstractGravity waves (GW) transport momentum flux (MF) and energy across the lower, middle and upper atmosphere. Global Navigation Satellite System (GNSS) radio occultation (RO) is one of the measuring techniques used onboard satellites to provide vertical temperature profiles with global and permanent coverage. These retrievals may be applied in the study of GW. Most of the analysis methods provide absolute GWMF but are missing its net direction. This happens because the procedures can deduce the orientation but not the propagation sign of GW and hence the full direction of MF. We apply here a method that allows the net calculation with four close in space and time RO soundings (quartets). We use about 10,000 daily retrievals from 1 March 2022 to 28 February 2023 to study the seasonal and latitudinal characteristics of net GWMF in the height interval from 20 to 35 km. About 600 quartets were found. The calculated zonal MF and drag exhibited negative minima at middle and high latitudes during winter in the Southern Hemisphere. This well‐known characteristic is usually mainly assigned to orographic sources. A similar intensity in zonal MF and drag is found during the same season at low latitudes. Meridional components are generally less significant. Besides finding the correct sign of the GWMF and the corresponding forcing on the mean flow, the quartets method also allows the determination of the horizontal and vertical wavelengths, the amplitude and sign of the vertical wave phase velocity and the intrinsic frequencies. The global statistics of these parameters are shown and each one exhibits a similar distribution shape across latitude bands. Large differences in the frequency of cases in vertical phase velocity sign appear only at low and high positive latitudes. The most even distribution of GW intrinsic frequency is found at low latitudes. We estimate that absolute MF calculations by methods assuming only upward GW propagation may produce a bias not larger than 40%. The increase of satellite measuring devices achieved in the last years due to the release of new missions led to a high spatial and temporal density of profiles that may allow the attainment of net GWMF climatologies over a seasonal time scale and about 4,000 km latitude bands but this performance may be even improved if the amount of retrievals continues to rise.

Mosquito population dynamics is shaped by the interaction among larval density, season, and land use.

Understanding how variation in key abiotic and biotic factors interact at spatial scales relevant for mosquito fitness and population dynamics is crucial for predicting current and future mosquito distributions and abundances, and the transmission potential for human pathogens. However, studies investigating the effects of environmental variation on mosquito traits have investigated environmental factors in isolation or in laboratory experiments that examine constant environmental conditions that often do not occur in the field. To address these limitations, we conducted a semi-field experiment in Athens, Georgia using the invasive Asian tiger mosquito ( Aedes albopictus ). We selected nine sites that spanned natural variation in impervious surface and vegetation cover to explore effects of the microclimate (temperature and humidity) on mosquitoes. On these sites, we manipulated conspecific larval density at each site. We repeated the experiment in the summer and fall. We then evaluated the effects of land cover, larval density, and time of season, as well as interactive effects, on the mean proportion of females emerging, juvenile development time, size upon emergence, and predicted per capita population growth (i.e., fitness). We found significant effects of larval density, land cover, and season on all response variables. Of most note, we saw strong interactive effects of season and intra-specific density on each response variable, including a non-intuitive decrease in development time with increasing intra-specific competition in the fall. Our study demonstrates that ignoring the interaction between variation in biotic and abiotic variables could reduce the accuracy and precision of models used to predict mosquito population and pathogen transmission dynamics, especially those inferring dynamics at finer-spatial scales across which transmission and control occur.

Comparative assessment of dry- and humid-heat extremes in a warming climate: Frequency, intensity, and seasonal timing

Dry and humid-heat extremes are two types of heat extremes, each exhibiting unique climatological characteristics and impacts on different sectors of society. Using historical simulations and projections produced under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) by models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we show a comparative assessment of the future changes in dry- and humid-heat extremes over global land. Relative to 1995–2014, projections for the mid-term future (2041–2060) and long-term future (2081–2100) periods suggest that most global regions will experience an increase in frequency and intensity of both dry- and humid-heat extremes, especially the tropical regions. In these future periods, the peak occurrences of dry- and humid-heat extremes in mid-to high-latitude regions often occur within the same month. However, there will be a one-to two-month gap between the peak occurrences of dry- and humid-heat extremes in tropical regions, primarily due to monsoonal circulations that introduce variability by causing dry-heat extremes before the onset of monsoons and humid-heat extremes as the monsoons commence. This suggests the need for sector-specific adaptation strategies during different periods of the year for tropical regions. Under both future scenarios, whether considering individual exposure or land area, the average level of exposure to extreme humid-heat days is projected to increase more significantly compared to dry-heat days. The above results highlight the risks associated with the intensification of humid heat in future climate scenarios and warrant the development of effective strategies to mitigate the adverse effects.

A data-driven Bayesian network model for pattern recognition of maritime accidents: A case study of Liaoning Sea area

A data-driven Bayesian network (BN) model is developed to capture the individual and collective impacts of accident influential factors (AIFs) on different attributes of maritime accidents, including accident type, severity, and loss. To develop the data-driven BN model, a dataset comprising 980 accidents from 2000 to 2023 in the Liaoning Sea area is initially established. Subsequently, the data-driven BN model is constructed using the Tree Augmented Network (TAN) learning algorithm and expectation-maximization (EM) algorithm. Finally, comprehensive BN analysis encompassing strength of influence assessment, sensitivity analysis, scenario simulation, and model verification is conducted based on the developed TAN-BN model. The results demonstrate that distinct types of maritime accidents exhibit varying sensitivities to seasonal variations and time of day; contact and groundings are primarily encountered by general cargo ships while fire and explosions are more prevalent among bulk carriers. Fishing boats tend to be vulnerable to collisions whereas sinking accidents are most commonly associated with sand carriers.

Utilization of typha-boards for preserving the historic wooden houses typical of the region in the Thai city of Chiang Mai (Thailand)

ABSTRACT The quality of life in the historic wooden houses typical of the city of Chiang Mai in northern Thailand is significantly reduced by climate changes and the intense air pollution during the Haze Season (time of slash-and-burn). The traditional indirect cooling system through strong natural infiltration ventilation in traditional houses is severely limited in its function due to changes in the local urban microclimate. The low comfort level leads to increased use of electric air conditioning, resulting in substantial energy consumption and the formation of urban heat islands and more and more often to the loss of historic Thai wooden houses by demolition.

Storylines for Global Hydrologic Drought Within CMIP6

AbstractFuture global increases in the duration and severity of hydrologic drought present an emerging challenge for water resource management. However, projected changes to drought within global climate models are often complex, including potentially co‐occurring changes to the timing, duration, and severity of drought. Here, we apply a storyline approach for interpreting projections of future hydrologic drought to identify coherent narratives that include runoff trends, shifts in the seasonal drought timing, increases in multi‐year drought frequency, and increased drought severity within the Coupled Model Intercomparison Project Phase 6. We develop a framework to classify future drought storylines (2015–2100) and quantify model consensus to determine the most‐likely “dominant” storyline under four emission scenarios Shared Socioeconomic Pathways (SSPs) 1–2.6, 2–4.5, 3–7.0, and 5–8.5. Under a low‐emission scenario (SSPs 1–2.6) approximately one third of the land‐area is projected to be impacted by a dominant storyline of minimally detectable runoff trend paired with increased frequency of multi‐year drought. However, under the highest‐emission scenarios (SSPs 5–8.5), the most likely storyline shifts to an increase in multi‐year drought frequency, increased severity of drought, and negative long‐term runoff trends for 62% of the area in those same regions. Shifts in the seasonal timing of drought are a component of dominant storylines for the northern latitudes across all emission scenarios. These results provide an alternative mode of interpretation of co‐occurring changes to the features of future drought, framed in a way to support regional adaptation strategies to mitigate future drought impacts.

Pollinator competition and the contingency of nectar depletion during an early spring resource pulse.

Concerns about competition between pollinators are predicated on the assumption of floral resource limitation. Floral resource limitation, however, is a complex phenomenon involving the interplay of resource production by plants, resource demand by pollinators, and exogenous factors-like weather conditions-that constrain both plants and pollinators. In this study, we examined nectar limitation during the mass flowering of rosaceous fruit trees in early spring. Our study was set in the same region as a previous study that found severe nectar limitation in summer grasslands. We used this seasonal contrast to evaluate two alternative hypotheses concerning the seasonal dynamics of floral resource limitation: either (H1) rates of resource production and consumption are matched through seasonal time to maintain a consistent degree of resource limitation, or (H2) a mismatch of high floral resource production and low pollinator activity in early spring creates a period of relaxed resource limitation that intensifies later in the year. We found generally much lower depletion in our spring study compared to the near 100% depletion found in the summer study, but depletion rates varied markedly through diel time and across sampling days, with afternoon depletion rates sometimes exceeding 80%. In some cases, there were also pronounced differences in depletion rates across simultaneously sampled floral species, indicating different degrees of nectar exploitation. These findings generally support the seasonal mismatch hypothesis (H2) but underscore the complex contingency of nectar depletion. The challenge of future work is to discern how the fluctuation of resource limitation across diel, inter-diel, and seasonal time scales translates into population-level outcomes for pollinators.

Long-term pollen season trends of Fraxinus (ash), Quercus (oak) and Ambrosia artemisiifolia (ragweed) as indicators of anthropogenic climate change impact

The ongoing climatic change, together with atmospheric pollution, influences the timing, duration and intensity of pollen seasons of some allergenic plant taxa. To study these influences, we correlated the trends in the pollen season characteristics of both woody (Fraxinus, Quercus) and herbaceous (Ambrosia) taxa from two pollen monitoring stations in Slovakia with the trends in meteorological factors and air pollutants during the last two decades. In woody species, the increased temperature during the formation of flower buds in summer and autumn led to an earlier onset and intensification of next year’s pollen season, especially in Quercus. The increase of relative air humidity and precipitation during this time also had a positive influence on the intensity of the pollen season of trees. The pollen season of the invasive herbaceous species Ambrosia artemisiifolia was prolonged by increased temperature and humidity during the summer and autumn of the same year, which extended the blooming period and delayed the end of the pollen season. From the studied air pollutants, only three were found to correlate with the intensity of the pollen season of the studied taxa, CO − positively and SO2 and NO2 − negatively. It is important to study these long-term trends since they not only give us valuable insight into the response of plants to changing conditions but also enable the prognosis of the exacerbations of pollen-related allergenic diseases.

Timing patterns of initial respiratory syncytial virus infection and factors influencing disease severity in hospitalized infants with different health status.

This study aimed to determine the timing patterns of the initial respiratory syncytial virus (RSV) infection and to identify the factors influencing disease severity in infants of varying health status. A retrospective study was conducted at the Affiliated Children's Hospital of Chongqing Medical University from 2012 to 2022. The timing of the first RSV infection was estimated in infants with differing health status using correlation analysis, considering their birth time. Logistic regression was utilized to identify factors influencing severe RSV infection in these infants.RSV detection primarily occurred in the winter and spring. Epidemic season and peak timing of RSV were not significantly affected by health status or the COVID-19 pandemic. A strong positive correlation was observed between the age at RSV infection and the interval from birth to the RSV peak season. Infants born during the RSV epidemic season exhibited a higher likelihood of infection within the first 2 months postbirth. In contrast, those born outside the RSV epidemic season were more susceptible to infection during the subsequent peak. Notably, infants with pre-existing health conditions contracted RSV at an earlier age compared to their healthy counterparts. Among healthy infants, severe RSV infection was associated with sex, age, and timing of infection. For infants with underlying conditions, severe RSV infection was primarily related to age and timing of infection. The initial timing of RSV infection in infants varied depending on their health status. Young age and infection timing during the RSV epidemic season were significant risk factors for severe RSV infection. These findings provide a theoretical basis for optimizing immunization strategies for infants with diverse health conditions.

Influence of Seasonal Level Shift Outlier on the Residuals of SARMA(1, 1) Model

Time series often contain discrepant observations which are called outliers. Seasonal Level Shift (SLS) outlier is one such which occurs in seasonal time series. And the importance of residuals in time series model building is well known. Thus in this paper, through analytical expressions, we establish the effect of the presence of an SLS outlier on the residuals of the Seasonal Autoregressive Moving Average model of order (1, 1) (SARMA(1, 1)), followed by a simulation study. One of the principle conclusions of this paper is that the SLS outlier not only affects the residuals at its time of incidence but also at the subsequent seasons. And, the extent of the effect depends on the magnitude of the outlier and parameters of the underlying model.. KEYWORDS :Time series, Seasonal level shift, Residuals, Seasonal autoregressive moving average.

Extreme drought with seasonal timing consistently promotes CH4 uptake through inconsistent pathways in a temperate grassland, China

Extreme drought with seasonal timing consistently promotes CH4 uptake through inconsistent pathways in a temperate grassland, China

Seasonal and Time-Series Analysis of Road Traffic Accidents

Road Traffic Accidents (RTAs) contribute a significant and escalating rate in harm, hospitalization, and mortality. This study assesses the seasonal trend and time series analysis of RTAs cases brought to casualty department of Liaquat University Hospital, Hyderabad, Pakistan. Objective: To conduct seasonal and time series analysis of RTA cases at Casualty Department of Liaquat University Hospital Hyderabad. Methods: Nine hundred and sixty-six road traffic accidents-related cases were examined through prospective study at casualty department, Liaquat University Hospital, Hyderabad, Pakistan from January 2020 to December 2022. The seasonal trend was demonstrated by drawing seasonal index plot and time series plot. Results: The largest occurrences of RTA-related cases were reported in the month of December, having 103 total cases with a mean of 34.33 ± 18.92. July had the second highest frequency of RTAs-related cases, with a total of 94 cases having a mean of 31 ± 5.43 cases. The second-highest number of cases was observed in July and November with approximately equal mean number of RTAs. The time series analysis demonstrated an increasing trend of RTAs-related cases in the last three months of the year. Conclusions: The number of cases connected to RTAs increased during seasons of extreme weather, such as the summer and winter, and it also showed an increasing tendency in the last quarter of 2022.

Multiscale estimation of the cooling effect of urban greenspace in subtropical and tropical cities

Urban greenspace has been widely recognized for its beneficial role in mitigating the urban heat island (UHI) effect and enhancing human thermal comfort. However, understanding on the thermal effects of greenspace still remains limited, particularly in the areas of regional differences, scale issues, and modeling approaches. This study employs a multiscale approach to evaluate the cooling effect of greenspace in Hong Kong and Singapore by combining remote sensing techniques at the territory level, a hybrid modeling approach at the urban district level, and field measurements at the micro level. Results demonstrate that the approach generally performed well in evaluating the relationship between cooling effect and various explanatory variables. At the city scale, the average reduction in LST by greenspace (△LSTG−UF) was found to be 3.08 ℃ in Hong Kong and 2.84 ℃ in Singapore, respectively. However, the cooling effect of greenspace is much reduced at the urban district level. Of the evaluated urban districts, greenspace was found to have a LST reduction effect ranging from 0.27 to 1.56 ℃ in Hong Kong and from −0.02–2.69 ℃ in Singapore, respectively. At the micro level, the study reveals that building height and greenspace volume have the most significant influence on ambient temperature variations during the hottest time of the summer season. These findings not only highlight the importance of considering scale and time variations when evaluating the thermal effects of greenspace but also help identify effective greenspace deployment strategies to combat excessive urban heat.

Evaluation of Gridded Rainfall Products in Three West African Basins

In recent years, accessing rainfall data from ground observation networks maintained by national meteorological services in West Africa has become increasingly challenging. This is primarily due to high acquisition costs and the often sparse distribution of rainfall gauges across the region, which limits their use in hydrological studies and related research. At the same time, the rising availability of precipitation products derived from satellite/earth observations, reanalysis datasets, and in situ measurements presents exciting prospects for hydrological applications. Nonetheless, these datasets constitute indirect measurements, necessitating rigorous validation against ground-based rainfall data. This study comprehensively assesses twenty-three gridded rainfall products, including sixteen from satellites, six from reanalysis data, and one from in situ measurements, across the Senegal, Gambia, and Casamance River basins. Performance evaluation is conducted across distinct climatic zones, both pre- and post-resampling against observed rainfall data gathered from forty-nine rainfall stations over a six-year period (2003–2008). Evaluation criteria include the Kling–Gupta Efficiency (KGE) and Percentage of Bias (PBIAS) metrics, assessed at daily, monthly, and seasonal time steps. The results reveal distinct performance levels among the evaluated rainfall products. RFE, ARC2, and CPC notably yield the highest KGE scores at the daily time step, while GPCP, CHIRP, CHIRPS, RFE, MSWEP, ARC2, CPC, TAMSAT, and CMORPHCRT demonstrate superior performance at the monthly time step. During the rainy season, these products generally exhibit robustness. However, rainfall estimates derived from reanalysis datasets (ERA5, EWEMBI, MERRA2, PGF, WFDEICRU, and WFDEIGPCC) perform poorly in the studied basins. Based on the PBIAS metric, most products tend to underestimate precipitation values, while only PERSIANN and PERSIANNCCS lead to significant overestimations. Spatially, optimal performance of the products is observed in the Casamance basin and the Sudanian and Sahelian climatic zones within the Gambia and Senegal basins. Conversely, in the Guinean zone of the Gambia and Senegal Rivers, the rainfall products displayed the poorest performance.

Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA

Abstract. Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts on reef species, habitats, restoration, and mitigation, yet a global knowledge gap remains due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h−1 winds and waves greater than 8 m on 10 September 2017, directly impacting the Florida Reef Tract (FRT) and providing an opportunity to perform a unique comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and multibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 area along the lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to storm impacts. Elevation change was calculated at over 4 million point locations across 10 habitat types within this study area for two time periods using data collected (1) approximately 1 year before the passing of Irma and 3 to 6 months following the storm's impact as well as (2) 3 to 6 months after and up to 16.5 months after the storm. Elevation change data were then used to generate triangulated irregular network (TIN) models in ArcMap to calculate changes in seafloor volume during each time period. Our results indicate that Hurricane Irma was primarily a depositional event that increased mean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported primarily west-southwest (WSW) and downslope, modifying geomorphic seafloor features including the migration of sand waves and rubble fields, formation of scour marks in shallow seagrass habitats, and burial of seagrass and coral-dominated habitats. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was observed across all habitats with mean elevation change of −0.15 m and net volume change up to −2.46 Mm3. Rates of elevation change during this post-storm period were 1 to 2 orders of magnitude greater than decadal and multi-decadal rates of change in the same location, and changes showed erosion of approximately 50 % of sediment deposited during the storm event as seafloor sediment distribution began to re-equilibrate to non-storm sea-state conditions. Our results suggest that higher-resolution elevation change data collected over seasonal and annual time periods could enhance characterization and understanding of short-term and long-term rates and processes of seafloor change.

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, 9months 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.