scholarly journals Índice de Tendências Climáticas de Longo Prazo em Área Urbana na Amazônia Oriental

2021 ◽  
Vol 14 (6) ◽  
pp. 3378
Author(s):  
Pedro Hugo Oliveira Moreira ◽  
Alan Cavalcanti da Cunha ◽  
Antonio Carlos Lola da Costa
Keyword(s):  
Climate Change ◽  
Change Detection ◽  
Urban Area ◽  
Air Temperature ◽  
Climate Indices ◽  
Data Series ◽  
Historical Series ◽  
The City ◽  
Climate Change Detection

Esta pesquisa tem como objetivo analisar a variabilidade e a tendência de variáveis meteorológicas no longo prazo para caracterizar o clima urbano da cidade de Macapá-AP. Compreender a variabilidade  dos índices climáticos em ambientes urbanos tende a mostrar possíveis interferências na qualidade de vida dos moradores locais, bem como torna possível comparar a realidade das cidades amazônicas em um contexto regional, nacional e mundial, contribuindo ao debate acadêmico. No presente caso as variáveis-chave são a temperatura do ar e a precipitação pluviométrica. A metodologia consiste nas seguintes etapas: a) coleta e consistência da série de dados por um período contínuo de 52 anos para o Estado do Amapá (1968 – 2020), b) a utilização do aplicativo RClimDex 1.1/IPCC para estimar as variações e as tendências climáticas locais utilizando-se 27 parâmetros climáticos extremos previstos pela equipe de peritos do CCI/CLIVAR e Climate Change Detection Monitoring and Indices (ETCCDMI). Os resultados obtidos acusaram treze indicadores estatísticos significativos (p<0,05), sugerindo tendência generalizada da elevação da temperatura média do ar na zona urbana da cidade. Como consequência, estes indicadores mostraram não somente uma significativa elevação das temperaturas máximas, médias e mínimas, mas também quais são os indicadores mais coerentemente associados com tendências de aquecimento temporal de cidades amazônicas, tanto para períodos diurnos quanto para períodos noturnos. Esse comportamento dos indicadores confirma a hipótese de predisposição a formação de ilha de calor em Macapá. Esta tendência mudou significativamente a partir de 2010.   Index of Long Term Climate Trends in Urban Area in the Eastern AmazonA B S T R A C T This research aims to analyze the variability and trend of meteorological variables in the long term to characterize the urban climate of the city of Macapá-AP. Understanding the variability of climate indices in urban environments reveals possible interferences in the quality of life of the inhabitants, especially in urban locations. However, it has been relatively difficult to quantify trends in historical series that reliably represent climate indices relevant to the reality of Amazonian cities, both at a local and regional level. In the present case, the key variables analyzed were air temperature and rainfall. The methodology followed the following steps: a) collection and consistency of the data series over a continuous period of 52 years for the State of Amapá (1968–2020), b) using the data series by the RClimDex 1.1/IPCC application to estimate the local climate variations and trends using 27 extreme weather parameters predicted by the CCI/CLIVAR and Climate Change Detection Monitoring and Indices (ETCCDMI) team of experts. The results showed thirteen significant statistical indicators (p<0.05), suggesting a general trend towards an increase in the average air temperature in the urban area of the city. As a consequence, these indicators showed not only a significant increase in maximum, average and minimum temperatures, but also the indicators most coherently associated with temporal warming trends in Amazonian cities. So many that these effects seem to affect both the day and night periods, confirming the hypothesis of a predisposition to the formation of an urban heat island, with a significant change in this trend from 2010 onwards. Keywords: RClimDex 1.1, climate change indice, Macapá, Amapá

scholarly journals Analysis of rainfall variation under climate change in Miyun reservoir

2014 ◽  
Vol 18 (5) ◽  
pp. 1481-1485
Author(s):  
Xiao-Juan Chen ◽  
Xiao-Hua Yang ◽  
Jun He ◽  
Xing-Hui Xia ◽  
Never Mujere
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Water Source ◽  
Data Series ◽  
Elastic Analysis ◽  
Miyun Reservoir ◽  
Rainfall Variation ◽  
Reservoir Basin ◽  
The Relationship ◽  
The City

Miyun reservoir is a surface water source of the city of Beijing. This paper explores the relationship between reservoir basin runoff and climate change. Statistical analyses are employed to analyze the variations in rainfall, air temperature, and runoff in the reservoir basin. Results show uneven inter-annual variability in rainfall data series. Air temperature show a rising trend with 1993 and 1994 being the two significant mutation years. Runoff has been decreasing over the years. Based one inter-annual analysis, July and August had the largest runoff. Elastic analysis shows no significant relationship between rainfall and runoff.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method

2011 ◽  
Vol 24 (20) ◽  
pp. 5275-5291 ◽  
Author(s):  
Bettina C. Lackner ◽  
Andrea K. Steiner ◽  
Gabriele C. Hegerl ◽  
Gottfried Kirchengast
Keyword(s):  
Climate Change ◽  
Change Detection ◽  
Confidence Level ◽  
Geopotential Height ◽  
Radio Occultation ◽  
Lower Stratosphere ◽  
Quality Data ◽  
Climate Change Signal ◽  
High Quality ◽  
Climate Change Detection

Abstract The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere–lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995–2010 intermittently and 2001–10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 50°S–50°N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.

scholarly journals Analysis of climate change in Dnipropetrovsk Region

2017 ◽  
pp. 43-51
Author(s):  
V. V. Hrynchak
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Climate Changes ◽  
Absolute Temperature ◽  
Climatic Conditions ◽  
Annual Precipitation ◽  
Temperature And Precipitation ◽  
Weather Observations ◽  
Average Annual Temperature

The decision about writing this article was made after familiarization with the "Brief Climatic Essay of Dnepropetrovsk City (prepared based on observations of 1886 – 1937)" written by the Head of the Dnipropetrovsk Weather Department of the Hydrometeorological Service A. N. Mikhailov. The guide has a very interesting fate: in 1943 it was taken by the Nazis from Dnipropetrovsk and in 1948 it returned from Berlin back to the Ukrainian Hydrometeorological and Environmental Directorate of the USSR, as evidenced by a respective entry on the Essay's second page. Having these invaluable materials and data of long-term weather observations in Dnipro city we decided to analyze climate changes in Dnipropetrovsk region. The article presents two 50-year periods, 1886-1937 and 1961-2015, as examples. Series of observations have a uniform and representative character because they were conducted using the same methodology and results processing. We compared two main characteristics of climate: air temperature and precipitation. The article describes changes of average annual temperature values and absolute temperature values. It specifies the shift of seasons' dates and change of seasons' duration. We studied the changes of annual precipitation and peculiarities of their seasonable distribution. Apart from that peculiarities of monthly rainfall fluctuations and their heterogeneity were specified. Since Dnipro city is located in the center of the region the identified tendencies mainly reflect changes of climatic conditions within the entire Dnipropetrovsk region.

scholarly journals Understanding Urban Growth in Beijing-Tianjin-Hebei Region over the Past 100 Years Using Old Maps and Landsat Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3264 ◽  
Author(s):  
Shuang Li ◽  
Zhongqiu Sun ◽  
Yafei Wang ◽  
Yuxia Wang
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Spatial Data ◽  
Urban Expansion ◽  
Remote Sensing Images ◽  
The Past ◽  
Old Maps ◽  
The World ◽  
The City

Studying urban expansion from a longer-term perspective is of great significance to obtain an in-depth understanding of the process of urbanization. Remote sensing data are mostly selected to investigate the long-term expansion of cities. In this study, we selected the world-class urban agglomeration of Beijing-Tianjin-Hebei (BTH) as the study area, and then discussed how to make full use of multi-source, multi-category, and multi-temporal spatial data (old maps and remote sensing images) to study long-term urbanization. Through this study, we addressed three questions: (1) How much has the urban area in BTH expanded in the past 100 years? (2) How did the urban area expand in the past century? (3) What factors or important historical events have changed the development of cities with different functions? By comprehensively using urban spatial data, such as old maps and remote sensing images, geo-referencing them, and extracting built-up area information, a long-term series of urban built-up areas in the BTH region can be obtained. Results show the following: (1) There was clear evidence of dramatic urban expansion in this area, and the total built-up area had increased by 55.585 times, from 126.181 km2 to 7013.832 km2. (2) Continuous outward expansion has always been the main trend, while the compactness of the built-up land within the city is constantly decreasing and the complexity of the city boundary is increasing. (3) Cities in BTH were mostly formed through the construction of city walls during the Ming and Qing dynasties, and the expansion process was mostly highly related to important political events, traffic development, and other factors. In summary, the BTH area, similarly to China and most regions of the world, has experienced rapid urbanization and the history of such ancient cities should be further preserved with the combined use of old maps.

The «rainfall-runoff» system and its long-term fluctuations in the Siverskyi Donets River Basin (Ukraine)

2021 ◽  
Author(s):  
Hanna Bolbot ◽  
Vasyl Grebin
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
River Basin ◽  
Air Temperature ◽  
River Runoff ◽  
Annual Runoff ◽  
Annual Precipitation ◽  
Spring Flood ◽  
Current Period

&lt;p&gt;The current patterns estimation of the water regime under climate change is one of the most urgent tasks in Ukraine and the world. Such changes are determined by fluctuations in the main climatic characteristics - precipitation and air temperature, which are defined the value of evaporation. These parameters influence on the annual runoff distribution and long-term runoff fluctuations. In particular, the annual precipitation redistribution is reflected in the corresponding changes in the river runoff.&lt;br&gt;The assessment of the current state and nature of changes in precipitation and river runoff of the Siverskyi Donets River Basin was made by comparing the current period (1991-2018) with the period of the climatological normal (1961-1990).&lt;br&gt;In general, for this area, it was defined the close relationship between the amount of precipitation and the annual runoff. Against the background of insignificant (about 1%) increase of annual precipitation in recent decades, it was revealed their redistribution by seasons and separate months. There is a decrease in precipitation in the cold period (November-February). This causes (along with other factors) a decrease in the amount of snow and, accordingly, the spring flood runoff. There are frequent cases of unexpressed spring floods of the Siverskyi Donets River Basin. The runoff during March-April (the period of spring flood within the Ukrainian part of the basin) decreased by almost a third.&lt;br&gt;The increase of precipitation during May-June causes a corresponding (insignificant) increase in runoff in these months. The shift of the maximum monthly amount of precipitation from May (for the period 1961-1990) to June (in the current period) is observed.&lt;br&gt;There is a certain threat to water supply in the region due to the shift in the minimum monthly amount of precipitation in the warm period from October to August. Compared with October, there is a higher air temperature and, accordingly, higher evaporation in August, which reduces the runoff. Such a situation is solved by rational water resources management of the basin. The possibility of replenishing water resources in the basin through the transfer runoff from the Dnieper (Dnieper-Siverskyi Donets channel) and the annual runoff redistribution in the reservoir system causes some increase in the river runoff of summer months in recent decades. This is also contributed by the activities of the river basin management structures, which control the maintenance water users' of minimum ecological flow downstream the water intakes and hydraulic structures in the rivers of the basin.&lt;br&gt;Therefore, in the period of current climate change, the annual runoff distribution of the Siverskyi Donets River Basin has undergone significant changes, which is related to the annual precipitation redistribution and anthropogenic load on the basin.&lt;/p&gt;

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