Climate change or variability? The case of Yellow river as indicated by extreme maximum and minimum air temperature during 1960–2004

2008 ◽  
Vol 93 (1-2) ◽  
pp. 35-43 ◽  
Author(s):  
Q. Zhang ◽  
C.-Y. Xu ◽  
Z. Zhang ◽  
G. Ren ◽  
Y. D. Chen
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Yellow River ◽  
Minimum Air Temperature

scholarly journals Multi-Model Projections of Climate Change in Different RCP Scenarios in an Arid Inland Region, Northwest China

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 347 ◽  
Author(s):  
Ruotong Wang ◽  
Qiuya Cheng ◽  
Liu Liu ◽  
Churui Yan ◽  
Guanhua Huang
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Air Temperature ◽  
Northwest China ◽  
Heihe River Basin ◽  
Heihe River ◽  
Rcp Scenarios ◽  
Maximum Air Temperature ◽  
The Heihe River Basin ◽  
Minimum Air Temperature

Based on three IPCC (Intergovernmental Panel on Climate Change) Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), observed meteorological data, ERA-40 reanalysis data, and five preferred GCM (general circulation model) outputs selected from 23 GCMs of CMIP5 (Phase 5 of the Coupled Model Intercomparison Project), climate change scenarios including daily precipitation, maximum air temperature, and minimum air temperature from 2021 to 2050 in the Heihe River basin, which is the second largest inland river basin in Northwest China, were generated by constructing a statistical downscaling model (SDSM). Results showed that the SDSM had a good prediction capacity for the air temperature in the Heihe River basin. During the calibration and validation periods from 1961 to 1990 and from 1991 to 2000, respectively, the coefficient of determination (R2) and the Nash–Sutcliffe efficiency coefficient (NSE) were both larger than 0.9, while the root mean square error (RMSE) was within 20%. However, the SDSM showed a relative lower simulation efficiency for precipitation, with R2 and NSE values of most meteorological stations reaching 0.5, except for stations located in the downstream desert areas. Compared with the baseline period (1976–2005), changes in the annual mean precipitation simulated by different GCMs during 2021–2050 showed great difference in the three RCP scenarios, fluctuating from −10 to +10%, which became much more significant at seasonal and monthly time scales, except for the consistent decreasing trend in summer and increasing trend in spring. However, the maximum and minimum air temperature exhibited a similar increasing tendency during 2021–2050 in all RCP scenarios, with a higher increase in maximum air temperature, which increased as the CO2 concentration of the RCP scenarios increased. The results could provide scientific reference for sustainable agricultural production and water resources management in arid inland areas subject to climate change.

scholarly journals Historical Spatiotemporal Trends in Snowfall Extremes over the Canadian Domain of the Great Lakes Basin

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Janine A. Baijnath-Rodino ◽  
Claude R. Duguay
Keyword(s):  
Climate Change ◽  
Great Lakes ◽  
Air Temperature ◽  
National Laboratory ◽  
Wind Gust ◽  
Great Lakes Basin ◽  
Adaptation Studies ◽  
Oak Ridge ◽  
Spatiotemporal Trends ◽  
Minimum Air Temperature

The Laurentian Great Lakes Basin (GLB) is prone to snowfall events developed from extratropical cyclones or lake-effect processes. Monitoring extreme snowfall trends in response to climate change is essential for sustainability and adaptation studies because climate change could significantly influence variability in precipitation during the 21st century. Many studies investigating snowfall within the GLB have focused on specific case study events with apparent under examinations of regional extreme snowfall trends. The current research explores the historical extremes in snowfall by assessing the intensity, frequency, and duration of snowfall within Ontario’s GLB. Spatiotemporal snowfall and precipitation trends are computed for the 1980 to 2015 period using Daymet (Version 3) monthly gridded interpolated datasets from the Oak Ridge National Laboratory. Results show that extreme snowfall intensity, frequency, and duration have significantly decreased, at the 90% confidence level, more so for the Canadian leeward shores of Lake Superior than that of Lake Huron, for the months of December and January. To help discern the spatiotemporal trends is snowfall extremes, several trend analyses for lake-induced predictor variables were analysed for two cities, Wawa and Wiarton, along the snowbelts of Lakes Superior and Huron, respectively. These variables include monthly maximum and minimum air temperature, maximum wind gust velocity, lake surface temperature, and maximum annual ice cover concentration. Resultant significant increase in December’s maximum and minimum air temperature for the city of Wawa may be a potential reason for the decreased extreme snowfall trends.

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 Projection of Hydro-Climatic Extreme Events under Climate Change in Yom and Nan River Basins, Thailand

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 665
Author(s):  
Chanchai Petpongpan ◽  
Chaiwat Ekkawatpanit ◽  
Supattra Visessri ◽  
Duangrudee Kositgittiwong
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Extreme Events ◽  
River Basins ◽  
Annual Rainfall ◽  
Extreme Floods ◽  
Extreme Flood ◽  
Rcp 8.5 ◽  
Near Future ◽  
Far Future

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future.

scholarly journals Heatwaves and Summer Urban Heat Islands: A Daily Cycle Approach to Unveil the Urban Thermal Signal Changes in Lisbon, Portugal

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 292 ◽  
Author(s):  
Ana Oliveira ◽  
António Lopes ◽  
Ezequiel Correia ◽  
Samuel Niza ◽  
Amílcar Soares
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Wind Direction ◽  
Urban Heat Islands ◽  
Daily Cycle ◽  
Local Climate ◽  
Thermal Signal ◽  
Air Temperatures ◽  
Local Climate Change ◽  
Urban Heat

Lisbon is a European Mediterranean city, greatly exposed to heatwaves (HW), according to recent trends and climate change prospects. Considering the Atlantic influence, air temperature observations from Lisbon’s mesoscale network are used to investigate the interactions between background weather and the urban thermal signal (UTS) in summer. Days are classified according to the prevailing regional wind direction, and hourly UTS is compared between HW and non-HW conditions. Northern-wind days predominate, revealing greater maximum air temperatures (up to 40 °C) and greater thermal amplitudes (approximately 10 °C), and account for 37 out of 49 HW days; southern-wind days have milder temperatures, and no HWs occur. Results show that the wind direction groups are significantly different. While southern-wind days have minor UTS variations, northern-wind days have a consistent UTS daily cycle: a diurnal urban cooling island (UCI) (often lower than –1.0 °C), a late afternoon peak urban heat island (UHI) (occasionally surpassing 4.0 °C), and a stable nocturnal UHI (1.5 °C median intensity). UHI/UCI intensities are not significantly different between HW and non-HW conditions, although the synoptic influence is noted. Results indicate that, in Lisbon, the UHI intensity does not increase during HW events, although it is significantly affected by wind. As such, local climate change adaptation strategies must be based on scenarios that account for the synergies between potential changes in regional air temperature and wind.

scholarly journals Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products

2021 ◽  
Vol 13 (6) ◽  
pp. 1177
Author(s):  
Peijuan Wang ◽  
Yuping Ma ◽  
Junxian Tang ◽  
Dingrong Wu ◽  
Hui Chen ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Frost Damage ◽  
Estimation Model ◽  
Spring Frost ◽  
Tea Plants ◽  
Elevation Model ◽  
Minimum Air Temperature

Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3 × 3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage.

scholarly journals Response of LUCC on Runoff Generation Process in Middle Yellow River Basin: The Gushanchuan Basin

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1237 ◽  
Author(s):  
Caihong Hu ◽  
Li Zhang ◽  
Qiang Wu ◽  
Shan-e-hyder Soomro ◽  
Shengqi Jian
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Yellow River ◽  
Trend Test ◽  
Generation Process ◽  
Runoff Generation ◽  
Contribution Rate ◽  
Runoff Reduction ◽  
Runoff Change ◽  
Significant Downward Trend

Runoff reduction in most river basins in China has become a hotpot in recent years. The Gushanchuan river, a primary tributary of the middle Yellow river, Northern China, showed a significant downward trend in the last century. Little is known regarding the relative contributions of changing environment to the observed hydrological trends and response on the runoff generation process in its watershed. On the basis of observed hydrological and meteorological data from 1965–2010, the Mann-Kendall trend test and climate elasticity method were used to distinguish the effects of climate change and human activities on runoff in the Gushanchuan basin. The results indicate that the runoff in the Gushanchuan Basin has experienced significant declines as large as 77% from 1965 to 2010, and a mutation point occurred around 1997; the contribution rate of climate change to runoff change is 12.9–15.1%, and the contribution rate of human activities to runoff change is 84.9–87.1%. Then we divided long-term data sequence into two stages around the mutation point, and analyzed runoff generation mechanisms based on land use and cover changes (LUCC). We found that the floods in the Gushanchuan Basin were still dominated by Excess-infiltration runoff, but the proportion in 1965–1997 and 1998–2010 decreased gradually (68.46% and 45.83% in turn). The proportion of Excess-storage runoff and Mixed runoff has increased, which means that the runoff is made up of more runoff components. The variation law of the LUCC indicates that the forest area increased by 49.61%, the confluence time increased by 50.42%, and the water storage capacity of the watershed increased by 30.35%.

The cattle lice of Great Britain Part I. Biology, with special reference to Haematopinus eurysternus

Parasitology ◽  
1941 ◽  
Vol 33 (3) ◽  
pp. 331-342 ◽  
Author(s):  
H. J. Craufurd-Benson
Keyword(s):  
Life Cycle ◽  
Great Britain ◽  
Special Reference ◽  
Incubation Period ◽  
Air Temperature ◽  
Average Length ◽  
Starvation Period ◽  
Complete Life Cycle ◽  
Threshold Of Development ◽  
Minimum Air Temperature

1. The geographical distribution of cattle lice in Britain is recorded in detail. Bovicola bovis is the commonest and most widely distributed species in Britain.2. The incubation period for the eggs was found to be: Haematopinus eurysternus, 9–19 days (av. 12); Bovicola bovis, 7–10 days (av. 8); Linognathus vitula, 10–13 days; Solenopotes capillatus, 10–13 days. With eggs of H. eurysternus it was found that the higher the minimum air temperature the shorter was the incubation period.3. In H. eurysternus the average length of the instars was: 1st, 4 days; 2nd, 4 days; 3rd, 4 days; pre-oviposition period, 3–4 days. The average time for the complete life cycle, egg to egg, was 28 days.4. The maximum longevity of H. eurysternus on the host was: males, 10 days; females, 16 days. No males or females of H. eurysternus survived a starvation period of 72 hr. at 20° C. and R.H. 70 or 0–10° C. and R.H. 70–85; but some nymphs survived this period at 20° C. and R.H. 70, but none survived 96 hr. starvation.5. The maximum number of eggs recorded for one female was 24; and eggs were laid at the rate of 1–4 a day.6. The threshold of development of the eggs of H. eurysternus appears to be about 27·5° C.

Sign in / Sign up

Export Citation Format

Share Document