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1.
Environ Int ; 171: 107675, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36565571

RESUMO

BACKGROUND: Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models. METHODS: We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients. FINDINGS: We found significant associations between COVID-19 death and several air pollution exposures, including: PM2.5 mass, PM0.1 mass, PM2.5 nitrates, PM2.5 elemental carbon, PM2.5 on-road diesel, and PM2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM2.5 mass and PM2.5 nitrate to HR âˆ¼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects. INTERPRETATION: Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , COVID-19 , Humanos , Meteorologia , Poluição do Ar/efeitos adversos , Poluição do Ar/análise , Poluentes Atmosféricos/análise , Material Particulado/efeitos adversos , Material Particulado/análise , Fatores de Risco , California/epidemiologia , Nitratos , Exposição Ambiental/efeitos adversos
2.
Sci Total Environ ; 862: 160767, 2023 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-36493835

RESUMO

The COVID-19 epidemic has exerted significant impacts on human health, social and economic activities, air quality and atmospheric chemistry, and potentially on climate change. In this study, an online coupled regional climate-chemistry-aerosol model (RIEMS-Chem) was applied to explore the direct, indirect, and feedback effects of anthropogenic aerosols on radiation, boundary layer meteorology, and fine particulate matter during the COVID-19 lockdown period from 23 January to 8 April 2020 over China. Model performance was validated against a variety of observations for meteorological variables, PM2.5 and its chemical components, aerosol optical properties, as well as shortwave radiation flux, which demonstrated that RIEMS-Chem was able to reproduce the spatial distribution and temporal variation of the above variables reasonably well. During the study period, direct radiative effect (DRE) of anthropogenic aerosols was stronger than indirect radiative effect (IRE) in most regions north of the Yangtze River, whereas IRE dominated over DRE in the Yangtze River regions and South China. In North China, DRE induced larger changes in meteorology and PM2.5 than those induced by IRE, whereas in South China, the changes by IRE were remarkably larger than those by DRE. Emission reduction alone during the COVID-19 lockdown reduced PM2.5 concentration by approximately 32 % on average over East China. As a result, DRE at the surface was weakened by 15 %, whereas IRE changed little over East China, leading to a decrease in total radiative effect (TRE) by approximately 7 % in terms of domain average. The DRE-induced changes in meteorology and PM2.5 were weakened due to emission reduction, whereas the IRE-induced changes were almost the same between the cases with and without emission reductions. By aerosol radiative and feedback effects, the COVID-19 emission reductions resulted in 0.06 °C and 0.04 °C surface warming, 1.6 and 4.0 µg m-3 PM2.5 decrease, 0.4 and 1.3 mm precipitation increase during the lockdown period in 2020 in terms of domain average over North China and South China, respectively, whereas the lockdown caused negligible changes on average over East Asia.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , COVID-19 , Humanos , Material Particulado/análise , Poluentes Atmosféricos/análise , Meteorologia , Retroalimentação , Monitoramento Ambiental/métodos , Controle de Doenças Transmissíveis , Aerossóis e Gotículas Respiratórios , Poluição do Ar/análise , China/epidemiologia
3.
J Environ Sci (China) ; 127: 453-464, 2023 May.
Artigo em Inglês | MEDLINE | ID: mdl-36522077

RESUMO

Continuous aggravated surface O3 over North China Plain (NCP) has attracted widely public concern. Herein, we evaluated the effects of changes in aerosols, precursor emissions, and meteorology on O3 in summer (June) of 2015-2019 over NCP via 8 scenarios with WRF-Chem model. The simulated mean MDA8 O3 in urban areas of 13 major cities in NCP increased by 17.1%∼34.8%, which matched well with the observations (10.8%∼33.1%). Meanwhile, the model could faithfully reproduce the changes in aerosol loads, precursors, and meteorological conditions. A relatively-even O3 increase (+1.2%∼+3.9% for 24-h O3 and +1.0%∼+3.8% for MDA8 O3) was induced by PM2.5 dropping, which was consistent with the geographic distribution of regional PM2.5 reduction. Meanwhile, the NO2 reduction coupled with a near-constant VOCs led to the elevated VOCs/NOx ratios, and then caused O3 rising in the areas under VOCs-limited regimes. Therein, the pronounced increases occurred in Handan, Xingtai, Shijiazhuang, Tangshan, and Langfang (+10.7%∼+13.6% for 24-h O3 and +10.2%∼+12.2% for MDA8 O3); while the increases in other cities were 5.7%∼10.5% for 24-h O3 and 4.9%∼9.2% for MDA8 O3. Besides, the meteorological fluctuations brought about the more noticeable O3 increases in northern parts (+12.5%∼+13.5% for 24-h O3 and +11.2%∼+12.4% for MDA8 O3) than those in southern and central parts (+3.2%∼+9.3% for 24-h O3 and +3.7%∼+8.8% for MDA8 O3). The sum of the impacts of the three drivers reached 16.7%∼21.9%, which were comparable to the changes of the observed O3. Therefore, exploring reasonable emissions-reduction strategies is essential for the ozone pollution mitigation over this region.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Ozônio , Meteorologia , Poluição do Ar/análise , Poluentes Atmosféricos/análise , Monitoramento Ambiental , Ozônio/análise , Aerossóis , China , Material Particulado/análise
4.
Sci Total Environ ; 858(Pt 3): 160016, 2023 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-36368400

RESUMO

In the context of global warming, the plateau lakes generally expand, but some lakes in the southern Tibetan Plateau appear to shrinkage, such as Duoqing Co Lake. We analyzed Duoqing Co which is located on the Yadong-Gulu Rift zone and two surrounding lakes using satellite and meteorological data. Optical and radar images were used to construct a time series of these lakes from 1988 to 2021. By comparing the area changes of surrounding lakes, it is found that Como Chamling Lake has shrunk, while Puma Yum Co Lake has shown an expansion trend. The interference deformation results show that both sides of the Yadong-Gulu Rift zone where the Duoqing Co Lake is located have experienced strong uplift and subsidence, sinking in the east and uplifting in the west. Under the northward compression of the Indian plate, the blocks on both sides of the Yadong-Gulu Rift zone have been relatively displaced. The disappearance of Duoqing Co Lake could be attributed to the existence of leakage channels in the Yadong-Gulu Rift zone. The north-south rift zones of the Tibetan Plateau pass through the Qiangtang Basin, and some lakes in this basin are shrinking, which could be related to the leakage of these rift zones. This work provides a new perspective for studying lake changes on the Tibetan Plateau and is a good reference for studying the lake water cycle on the plateau.


Assuntos
Meteorologia , Uganda , Tibet
5.
Sci Total Environ ; 858(Pt 3): 160137, 2023 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-36375556

RESUMO

During 2015-2018, eight black carbon (BC) monitoring sites were established in Nepal and Bhutan to fill a significant data gap regarding BC measurement in Central Himalaya. This manuscript analyzes and presents data from these eight stations and one additional station on the Tibetan plateau (TP). Complex topography, varied emission sources, and atmospheric transport pathways significantly impacted the BC concentrations across these stations, with annual mean concentrations varying from 36 ng m-3 to 45,737 ng m-3. Higher annual mean concentrations (5609 ± 4515 ng m-3) were recorded at low-altitude sites than in other locations, with seasonal concentrations highest in the winter (7316 ± 2541 ng m-3). In contrast, the annual mean concentrations were lowest at high-altitude sites (376 ± 448 ng m-3); the BC concentrations at these sites peaked during the pre-monsoon season (930 ± 685 ng m-3). Potential source contributions to the total observed BC were analyzed using the absorption angstrom exponent (AAE). AAE analysis showed the dominance of biomass burning sources (>50 %), except in Kathmandu. By combining our data with previously published literature, we put our measurements in perspective by presenting a comprehensive assessment of BC concentrations and their variability over the Hindu Kush Himalayan (HKH) region. The BC levels in all three geographic regions, high, mid, and low altitude significantly influenced by the persistent seasonal meteorology. However, the mid-altitude stations were substantially affected by valley dynamics and urbanization. The low-altitude stations experienced high BC concentrations during the winter and post-monsoon seasons. Concentration weighted trajectory (CWT) and frequency analyses revealed the dominance of long-range transported pollution during winter over HKH, from west to east. South Asian sources remained significant during the monsoon season. During pre- and post-monsoon, the local, regional, and long-distance pollution varied depending on the location of the receptor site.


Assuntos
Meteorologia , Urbanização , Nepal , Carbono
6.
Sci Total Environ ; 858(Pt 3): 160216, 2023 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-36402316

RESUMO

Monitoring carbon dioxide (CO2) emissions of urban areas is increasingly important to assess the progress towards the Paris Agreement goals for climate neutrality. Cities are currently voluntarily developing their local inventories, however, the approaches used across different cities are not systematically assessed, present consistency issues, neglect the biogenic fluxes and have restricted spatial and temporal resolution. In order to assess the accuracy of the urban emission inventories and provide information which is useful for planning local climate change mitigation actions, high resolution modelling approaches combined or evaluated with atmospheric observations are needed. This study presents a new high-resolution bottom-up (BU) model which provides hourly maps of all major components contributing to the local urban surface CO2 flux (i.e. building emissions, traffic emissions, human respiration, soil respiration, plant respiration, plant photosynthetic uptake) and can therefore be used for direct comparison with in-situ atmospheric observations and development of local scale atmospheric inversion methodologies. The model design aims to be simple and flexible using inputs that are available in most cities, facilitating transferability to different locations. The inputs are primarily based on open geospatial datasets, census information, road traffic monitoring and basic meteorological parameters. The model is applied on the city centre of Basel, Switzerland, for the year 2018 and the results are compared to a local inventory. It is demonstrated that the model captures the highly dynamic spatiotemporal variability of the urban CO2 fluxes according to main environmental drivers, population activity dynamics and geospatial information proxies. The annual modelled emissions from buildings and traffic are estimated 14.8 % and 9 % lower than the respective information derived by the local inventory. The differences are mainly attributed to the emissions from the industrial areas and the highways which are beyond the geographical coverage of the model.


Assuntos
Dióxido de Carbono , Censos , Humanos , Cidades , Geografia , Meteorologia
7.
J Environ Sci (China) ; 126: 506-516, 2023 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-36503777

RESUMO

Deterioration of surface ozone (O3) pollution in Northern China over the past few years received much attention. For many cities, it is still under debate whether the trend of surface O3 variation is driven by meteorology or the change in precursors emissions. In this work, a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O3 trend and identify the key meteorological factors affecting O3 pollution in Tianjin, the biggest coastal port city in Northern China. After "removing" the meteorological fluctuations from the observed O3 time series, we found that variation of O3 in Tianjin was largely driven by the changes in precursors emissions. The meteorology was unfavorable for O3 pollution in period of 2015-2016, and turned out to be favorable during 2017-2021. Specifically, meteorology contributed 9.3 µg/m3 O3 (13%) in 2019, together with the increase in precursors emissions, making 2019 to be the worst year of O3 pollution since 2015. Since then, the favorable effects of meteorology on O3 pollution tended to be weaker. Temperature was the most important factor affecting O3 level, followed by air humidity in O3 pollution season. In the midday of summer days, O3 pollution frequently exceeded the standard level (>160 µg/m3) at a combined condition with relative humidity in 40%-50% and temperature > 31°C. Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O3 forecasting.


Assuntos
Conceitos Meteorológicos , Meteorologia , Umidade , Atmosfera , Cidades
8.
Nat Commun ; 13(1): 7437, 2022 Dec 02.
Artigo em Inglês | MEDLINE | ID: mdl-36460645

RESUMO

Diurnal rainfall offshore propagation (OP) shapes the timing and intensity of coastal rainfall and thus impacts both nature and society. Previous OP studies have rarely compared various coasts, and a consensus regarding physical mechanisms has not been reached on a global scale. Here, we provide the global climatology of observed OP, which propagates across ~78% of all coasts and accounts for ~59% of the coastal precipitation. Generally, OP is facilitated by low latitudes, high moisture conditions and offshore background winds. OP at low latitudes in a high-moisture environment is mainly caused by inertia-gravity waves due to the land-sea thermal contrast, whereas OP at higher latitudes is significantly influenced by background winds under trapped land-sea breeze circulation conditions. Slower near-shore OP might be modulated by density currents. Our results provide a guide for global OP hotspots and suggest relative contributions of mechanisms from a statistical perspective.


Assuntos
Doenças Cardiovasculares , Reprodução , Humanos , Consenso , Gravitação , Meteorologia
9.
Sci Rep ; 12(1): 21655, 2022 12 15.
Artigo em Inglês | MEDLINE | ID: mdl-36522406

RESUMO

Complex systems in biology, climatology, medicine, and economy hold emergent properties such as non-linearity, adaptation, and self-organization. These emergent attributes can derive from large-scale relationships, connections, and interactive behavior despite not being apparent from their isolated components. It is possible to better comprehend complex systems by analyzing cross-correlations between time series. However, the accumulation of non-linear processes induces multiscale structures, therefore, a spectrum of power-law exponents (the fractal dimension) and distinct cyclical patterns. We propose the Multifractal detrended cross-correlation heatmaps (MF-DCCHM) based on the DCCA cross-correlation coefficients with sliding boxes, a systematic approach capable of mapping the relationships between fluctuations of signals on different scales and regimes. The MF-DCCHM uses the integrated series of magnitudes, sliding boxes with sizes of up to 5% of the entire series, and an average of DCCA coefficients on top of the heatmaps for the local analysis. The heatmaps have shown the same cyclical frequencies from the spectral analysis across different multifractal regimes. Our dataset is composed of sales and inventory from the Brazilian automotive sector and macroeconomic descriptors, namely the Gross Domestic Product (GDP) per capita, Nominal Exchange Rate (NER), and the Nominal Interest Rate (NIR) from the Central Bank of Brazil. Our results indicate cross-correlated patterns that can be directly compared with the power-law spectra for multiple regimes. We have also identified cyclical patterns of high intensities that coincide with the Brazilian presidential elections. The MF-DCCHM uncovers non-explicit cyclic patterns, quantifies the relations of two non-stationary signals (noise effect removed), and has outstanding potential for mapping cross-regime patterns in multiple domains.


Assuntos
Fractais , Meteorologia , Fatores de Tempo , Projetos de Pesquisa , Brasil
10.
Artigo em Inglês | MEDLINE | ID: mdl-36497872

RESUMO

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


Assuntos
Secas , Meteorologia , Análise Espacial , Incerteza , Polônia
11.
Sensors (Basel) ; 22(23)2022 Dec 05.
Artigo em Inglês | MEDLINE | ID: mdl-36502206

RESUMO

Radiance observations are typically affected by biases that come mainly from instrument error (scanning or calibration) and inaccuracies of the radiative transfer model. These biases need to be removed for successful assimilation, so a bias correction scheme is crucial in the Numerical Weather Prediction (NWP) system. Today, most NWP centres, including the Bureau of Meteorology (hereafter, "the Bureau"), correct the biases through variational bias correction (VarBC) schemes, which were originally developed for global models. However, there are difficulties in estimating the biases in a limited-area model (LAM) domain. As a result, the Bureau's regional NWP system, ACCESS-C (Australian Community Climate and Earth System Simulator-City), uses variational bias coefficients obtained directly from its global NWP system ACCESS-G (Global). This study investigates independent radiance bias correction in the data assimilation system for ACCESS-C. We assessed the impact of using independent bias correction for the LAM compared with the operational bias coefficients derived in ACCESS-G between February and April 2020. The results from our experiment show no significant difference between the control and test, suggesting a neutral impact on the forecast. Our findings point out that the VarBC-LAM strategy should be further explored with different settings of predictors and adaptivity for a more extended period and over additional domains.


Assuntos
Meteorologia , Tempo (Meteorologia) , Austrália , Clima , Cidades
12.
Sci Rep ; 12(1): 20037, 2022 Nov 21.
Artigo em Inglês | MEDLINE | ID: mdl-36414682

RESUMO

Hemorrhagic fever with renal syndrome (HFRS), caused by hantavirus, is a serious public health problem in China. Despite intensive countermeasures including Patriotic Health Campaign, rodent control and vaccination in affected areas, HFRS is still a potential public health threat in China, with more than 10,000 new cases per year. Previous epidemiological evidence suggested that meteorological factors could influence HFRS incidence, but the studies were mainly limited to a specific city or region in China. This study aims to evaluate the association between monthly HFRS cases and meteorological change at the country level using a multivariate distributed lag nonlinear model (DLNM) from 2004 to 2018. The results from both univariate and multivariate models showed a non-linear cumulative relative risk relationship between meteorological factors (with a lag of 0-6 months) such as mean temperature (Tmean), precipitation, relative humidity (RH), sunshine hour (SH), wind speed (WS) and HFRS incidence. The risk for HFRS cases increased steeply as the Tmean between - 23 and 14.79 °C, SH between 179.4 and 278.4 h and RH remaining above 69% with 50-95 mm precipitation and 1.70-2.00 m/s WS. In conclusion, meteorological factors such as Tmean and RH showed delayed-effects on the increased risk of HFRS in the study and the lag varies across climate factors. Temperature with a lag of 6 months (RR = 3.05) and precipitation with a lag of 0 months (RR = 2.08) had the greatest impact on the incidence of HFRS.


Assuntos
Epidemias , Febre Hemorrágica com Síndrome Renal , Tempo (Meteorologia) , Humanos , China/epidemiologia , Febre Hemorrágica com Síndrome Renal/epidemiologia , Incidência , Meteorologia
13.
Proc Natl Acad Sci U S A ; 119(46): e2210481119, 2022 11 16.
Artigo em Inglês | MEDLINE | ID: mdl-36343255

RESUMO

How clouds respond to anthropogenic sulfate aerosols is one of the largest sources of uncertainty in the radiative forcing of climate over the industrial era. This uncertainty limits our ability to predict equilibrium climate sensitivity (ECS)-the equilibrium global warming following a doubling of atmospheric CO2. Here, we use satellite observations to quantify relationships between sulfate aerosols and low-level clouds while carefully controlling for meteorology. We then combine the relationships with estimates of the change in sulfate concentration since about 1850 to constrain the associated radiative forcing. We estimate that the cloud-mediated radiative forcing from anthropogenic sulfate aerosols is [Formula: see text] W m-2 over the global ocean (95% confidence). This constraint implies that ECS is likely between 2.9 and 4.5 K (66% confidence). Our results indicate that aerosol forcing is less uncertain and ECS is probably larger than the ranges proposed by recent climate assessments.


Assuntos
Clima , Meteorologia , Aerossóis , Sulfatos , Oceanos e Mares
14.
Environ Monit Assess ; 195(1): 67, 2022 Nov 03.
Artigo em Inglês | MEDLINE | ID: mdl-36329360

RESUMO

In this study, the predictive power of three different machine learning (ML)-based approaches, namely, multi-gene genetic programming (MGGP), M5 model trees (M5Tree), and K-nearest neighbor algorithm (KNN), for long-term monthly reference evapotranspiration (ET0) prediction were investigated. The input data consist of monthly solar radiation (Rs), maximum air temperature (Tmax), and wind speed (Ws) derived from 163 meteorological stations in Turkey. Different input combinations were created and analyzed. The model's performance was evaluated using criteria such as Nash-Sutcliffe efficiency, Kling-Gupta efficiency, relative root mean squared error, mean absolute percentage error, and determination coefficient. Moreover, Taylor, radar, and boxplot diagrams were created. It was determined that the MGGP model outperformed both the M5Tree and the KNN models. The equation obtained from the MGGP model, for the best-performed combination of Rs-Tmax-Ws, was presented. The best weather conditions were obtained as 0.029 to 31.814 MJ/m2, - 5.8 to 45.7 °C, and 0.140 to 5.086 m/s for Rs, Tmax, and Ws, respectively. It was also found that the Rs was the most potent input variable for ET0 estimation while Ws was the weakest.


Assuntos
Monitoramento Ambiental , Aprendizado de Máquina , Turquia , Vento , Meteorologia
15.
Sci Rep ; 12(1): 20694, 2022 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-36450747

RESUMO

Countries depending on small-scale agriculture, such as Bangladesh, are susceptible to climate change and variability. Changes in the frequency and intensity of drought are a crucial aspect of this issue and the focus of this research. The goal of this work is to use SPI (standardized precipitation index) and SPEI (standardized precipitation evapotranspiration index) to investigate the differences in drought characteristics across different physiognomy types in Bangladesh and to highlight how drought characteristics change over time and spatial scales when considering different geomorphologies. This study used monthly precipitation and temperature data from 29 metrological stations for 39 years (1980-2018) for calculating SPI and SPEI values. To determine the significance of drought characteristic trends over different temporal and spatial scales, the modified Mann-Kendall trend test and multivariable linear regression (MLR) techniques were used. The results are as follows: (1) Overall, decreasing dry trend was found in Eastern hill regions, whereas an increasing drought trends were found in the in the rest of the regions in all time scaless (range is from - 0.08 decade-1 to - 0.15 decade-1 for 3-month time scale). However, except for the one-month time scale, the statistically significant trend was identified mostly in the north-central and northeast regions, indicating that drought patterns migrate from the northwest to the center region. (2) SPEI is anticipated to be better at capturing dry/wet cycles in more complex regions than SPI. (3) According to the MLR, longitude and maximum temperature can both influence precipitation. (4) Drought intensity increased gradually from the southern to the northern regions (1.26-1.56), and drought events occurred predominantly in the northwestern regions (27-30 times), indicating that drought meteorological hotspots were primarily concentrated in the Barind Tract and Tista River basin over time. Findings can be used to improve drought evaluation, hazard management, and application policymaking in Bangladesh. This has implications for agricultural catastrophe prevention and mitigation.


Assuntos
Secas , Meteorologia , Bangladesh , Análise Espaço-Temporal , Mudança Climática
16.
Sci Rep ; 12(1): 20691, 2022 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-36450818

RESUMO

Rainfall estimation over large areas is important for a thorough understanding of water availability, influencing societal decision-making, as well as being an input for scientific models. Traditionally, Australia utilizes a gauge-based analysis for rainfall estimation, but its performance can be severely limited over regions with low gauge density such as central parts of the continent. At the Australian Bureau of Meteorology, the current operational monthly rainfall component of the Australian Gridded Climate Dataset (AGCD) makes use of statistical interpolation (SI), also known as optimal interpolation (OI) to form an analysis from a background field of station climatology. In this study, satellite observations of rainfall were used as the background field instead of station climatology to produce improved monthly rainfall analyses. The performance of these monthly datasets was evaluated over the Australian domain from 2001 to 2020. Evaluated over the entire national domain, the satellite-based SI datasets had similar to slightly better performance than the station climatology-based SI datasets with some individual months being more realistically represented by the satellite-SI datasets. However, over gauge-sparse regions, there was a clear increase in performance. For a representative sub-domain, the Kling-Gupta Efficiency (KGE) value increased by + 8% (+ 12%) during the dry (wet) season. This study is an important step in enhancing operational rainfall analysis over Australia.


Assuntos
Clima , Meteorologia , Austrália , Estações do Ano , Água
19.
Environ Monit Assess ; 194(12): 883, 2022 Oct 14.
Artigo em Inglês | MEDLINE | ID: mdl-36239815

RESUMO

Drought is considered among the most perilous events with catastrophic consequences, particularly from the agro-economic point of view. These consequences are expected to exacerbate under the increasing meteorological aberrations due to changing climate, which necessitates investigating drought variabilities. This study presents a thorough spatiotemporal assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, Maharashtra, India. The precipitation data is extracted from the India Meteorological Department (IMD) gridded product, whereas actual evapotranspiration (ET) and Evaporative Stress Index (ESI) are obtained from Global Land Evaporation Amsterdam Model (GLEAM) datasets. Standardized Precipitation Index (SPI) is used to characterize drought occurrences at multiple time frames, whereas non-parametric tests, i.e., modified Mann-Kendall (MMK) and Sen's slope (SS) tests, are employed to detect trends. The results reveal the region to be prone to droughts, and SPI at a longer time frame (i.e., 12-monthly moving frame) can capture drought occurrences better than the shorter time frames, which can be attributed to the lesser randomness in the time series in the longer frame. A mix of positive/negative trends of SPI series are found for the monsoonal months; however, they are relatively more concentrated towards negative ZMMK. Hence, the Marathwada Region can be inferred to have exhibited a relatively increased tendency towards drought occurrences. The seasonal differences in mean values and trends of rainfall, ET, and ESI are discussed in detail. Since the Marathwada Region has a monsoon-dominated climate with high agricultural importance, the information reported in this study will help in devising water management strategies to minimize the repercussions of droughts.


Assuntos
Secas , Monitoramento Ambiental , Agricultura , Índia , Meteorologia
20.
Environ Monit Assess ; 194(12): 902, 2022 Oct 17.
Artigo em Inglês | MEDLINE | ID: mdl-36251084

RESUMO

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


Assuntos
Secas , Rios , Monitoramento Ambiental/métodos , Meteorologia , Solo
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