scholarly journals Four Decades of Surface Temperature, Precipitation, and Wind Speed Trends over Lakes of Greece

2021 ◽  
Vol 13 (17) ◽  
pp. 9908
Author(s):  
Konstantinos Stefanidis ◽  
George Varlas ◽  
Anastasios Papadopoulos ◽  
Elias Dimitriou
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Surface Temperature ◽  
Average Rate ◽  
Generalized Additive Models ◽  
Climatic Changes ◽  
Rate Of Change ◽  
Additive Models ◽  
Freshwater Bodies ◽  
Of Greece

Climate change is known to affect world’s lakes in many ways. Lake warming is perhaps the most prominent impact of climate change but there is evidence that changes of precipitation and wind speed over the surface of the lakes may also have a significant effect on key limnological processes. With this study we explored the interannual trends of surface temperature, precipitation, and wind speed over 18 lakes of Greece using ERA5-Land data spanning over a period of almost four decades. We used generalized additive models (GAMs) to conduct time-series analysis in order to identify significant trends of change. Our results showed that surface temperature has significantly increased in all lakes with an average rate of change for annual temperature of 0.43 °C decade−1. With regard to precipitation, we identified significant trends for most lakes and particularly we found that precipitation decreased during the first two decades (1981–2000), but since 2000 it increased notably. Finally, wind speed did not show any significant change over the examined period with the exception for one lake. In summary, our work highlights the major climatic changes that have occurred in several freshwater bodies of Greece. Thus, it improves our understanding on how climate change may have impacted the ecology of these important ecosystems and may aid us to identify systems that are more vulnerable to future changes.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

scholarly journals Perbandingan Model GAM dan Gamboost dalam Fitting Dataset Sea Surface Temperature

2018 ◽  
Vol 18 (1) ◽  
pp. 39-50
Author(s):  
Miftahuddin Miftahuddin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Risk Model ◽  
Generalized Additive Models ◽  
Information Criteria ◽  
Additive Models ◽  
Sea Surface ◽  
Akaike Information Criteria ◽  
Fitting Model

Fitting model GAM (generalized additive models) dan Gamboost (generalized additive models by boosting) untuk dataset SST (sea surface temperature) dimaksudkan sebagai upaya mencapai perbaikan fitting model terhadap data SST. Secara umum, model GAM dapat memvisualisasikan masing-masing kovariat, sedangkan model gamboost dapat memvisualisasikan lebih detail kovariatnya dalam beberapa bentuk, baik secara linier dan nonlinier. Pengukuran performance yang digunakan terhadap model adalah nilai AIC (Akaike Information Criteria) dan CV-risk. Model GAM dengan boosting menunjukkan lebih sesuai dalam struktur model, pemilihan model terbaik dan seleksi variabel pada dataset SST. Fitting model GAM dapat menghasilkan pola dan trend masing-masing kovariat meskipun memiliki beberapa gap, sedangkan pada model gamboost memiliki lebih banyak pilihan simultan dalam bentuk linier, nonlinier dan smooth untuk masing-masing kovariat. Kedua pendekatan fitting memiliki kelebihan yang dapat saling melengkapi dalam memodelkan dataset SST.

scholarly journals Assessment of the Effects of Climate Change on Evapotranspiration with an Improved Elasticity Method in a Nonhumid Area

2018 ◽  
Vol 10 (12) ◽  
pp. 4589 ◽  
Author(s):  
Lei Tian ◽  
Jiming Jin ◽  
Pute Wu ◽  
Guo-yue Niu
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Energy Budget ◽  
Yellow River ◽  
Rate Of Change ◽  
Climatic Variables ◽  
Accurate Estimation ◽  
Net Radiation ◽  
River Watershed ◽  
Significant Downward Trend

Climatic elasticity is a crucial metric to assess the hydrological influence of climate change. Based on the Budyko equation, this study performed an analytical derivation of the climatic elasticity of evapotranspiration (ET). With this derived elasticity, it is possible to quantitatively separate the impacts of precipitation, air temperature, net radiation, relative humidity, and wind speed on ET in a watershed. This method was applied in the Wuding River Watershed (WRW), located in the center of the Yellow River Watershed of China. The estimated rate of change in ET caused by climatic variables is −10.69 mm/decade, which is close to the rate of change in ET (−8.06 mm/decade) derived from observable data. The accurate estimation with the elasticity method demonstrates its reliability. Our analysis shows that ET in the WRW had a significant downward trend, but the ET ratio in the WRW has increased continually over the past 52 years. Decreasing precipitation is the first-order cause for the reduction of ET, and decreasing net radiation is the secondary cause. Weakening wind speed also contributed to this reduction. In contrast, regional warming led to an increase in ET that partly offset the negative contributions from other climatic variables. Moreover, reforestation can affect the energy budget of a watershed by decreasing albedo, compensating for the negative influence of global dimming. The integrated effect from precipitation and temperature can affect the energy budget of a watershed by causing a large fluctuation in winter albedo.

scholarly journals One decade of parallel fine (PM<sub>2.5</sub>) and coarse (PM<sub>10</sub>–PM<sub>2.5</sub>) particulate matter measurements in Europe: trends and variability

2012 ◽  
Vol 12 (7) ◽  
pp. 3189-3203 ◽  
Author(s):  
I. Barmpadimos ◽  
J. Keller ◽  
D. Oderbolz ◽  
C. Hueglin ◽  
A. S. H. Prévôt
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Stationary Point ◽  
Generalized Additive Models ◽  
Negative Relationship ◽  
Additive Models ◽  
Wind Speeds ◽  
East Wind ◽  
Pm10 And Pm2.5 ◽  
Meteorological Observations

Abstract. The trends and variability of PM10, PM2.5 and PMcoarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated. Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PMcoarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM2.5 except for one station, which exhibits a stationary point. Considering PMcoarse, concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM10 and PM2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are −0.4 μg m−3 yr−1 for PM10 and PM2.5 size fractions. PMcoarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PMcoarse in addition to PM2.5 can result in a faster decrease of PM10 in the future. The trends of the 90th quantile of PM10 and PM2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (−0.6 μg m−3 yr−1).

Analyzing the potential impacts of climate change on rainfed wheat production in Hamedan Province, Iran, via generalized additive models

2015 ◽  
Vol 7 (1) ◽  
pp. 212-223 ◽  
Author(s):  
Hassan Mohammadian Mosammam ◽  
Ali M. Mosammam ◽  
Mozaffar Sarrafi ◽  
Jamileh Tavakoli Nia ◽  
Hassan Esmaeilzadeh
Keyword(s):  
Climate Change ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Climatic Variables ◽  
Climate Change Scenarios ◽  
Wheat Production ◽  
Temperature And Precipitation ◽  
Hamedan Province ◽  
Rainfed Wheat ◽  
Ipcc Sres

Climate change is one of the greatest challenges in the 21st century and the agriculture sector is very vulnerable to this phenomenon. Since wheat is the most important cereal crop in Iran, we aim to analyze the potential impact of climatic variables (temperature and precipitation) on rainfed wheat productivity in Hamedan Province, Iran. For this purpose, generalized additive models have been used to model yields of rainfed wheat based on climatic variables during 2004–2012. Then, based on sensitivity of rainfed wheat to temperature and precipitation in this period, we predict the potential effects of climate change on rainfed wheat yield under the IPCC SRES A1FI and B1 climate change scenarios. Results suggest that yields of rainfed wheat would decrease in all Hamedan's counties primarily because of decreasing October to June precipitation and higher temperature. As a result, it is predicted that the yield of rainfed wheat in Hamedan under the A1F1 and B1 scenarios will fall by 41.3% and 20.6%, respectively, in the 2080s. In other words, according to the A1F1 scenario, in the 2080s, Hamedan Province's rainfed wheat production will decline from 1090 kg/ha to 639 kg/ha and under the B1 scenario to 865 kg/ha.

scholarly journals One decade of parallel PM10 and PM2.5 measurements in Europe: trends and variability

2012 ◽  
Vol 12 (1) ◽  
pp. 1-43 ◽  
Author(s):  
I. Barmpadimos ◽  
J. Keller ◽  
D. Oderbolz ◽  
C. Hueglin ◽  
A. S. H. Prévôt
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Stationary Point ◽  
Generalized Additive Models ◽  
Negative Relationship ◽  
Additive Models ◽  
Wind Speeds ◽  
East Wind ◽  
Pm10 And Pm2.5 ◽  
Meteorological Observations

Abstract. The trends and variability of PM10, PM2.5 and PMcoarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated. Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PMcoarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM2.5 except for one station, which exhibits a stationary point. Considering PMcoarse, concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM10 and PM2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are −0.4 μg m−3 yr−1 for PM10 and PM2.5 size fractions. PMcoarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PMcoarse in addition to PM2.5 can result in a faster decrease of PM10 in the future. The trends of the 90th quantile of PM10 and PM2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (−0.6 μg m−3 yr−1).

Long-term changes in inland water surface temperature across China based on remote sensing data

2020 ◽  
Author(s):  
Rui Wang ◽  
Xin Yan ◽  
Zhenguo Niu ◽  
Wei Chen
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Surface Temperature ◽  
Water Surface ◽  
Annual Variation ◽  
Average Rate ◽  
Transform Method ◽  
Inland Water ◽  
The Past ◽  
Water Surface Temperature

AbstractWater surface temperature is a direct indication of climate change. However, it is not clear how have China’s inland waters responded to climate change in the past using a consistent method on a national scale. In this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2015 to study the temporal and spatial variation characteristics of water surface temperature in China using the wavelet transform method. The results showed the following: (1) the freezing date of China inland water has shown a significant delaying trend during the past 16 years with an average rate of -1.5 d/a; (2) the shift of 0°C isotherm position of surface water across China has clear seasonal changes, which first moved eastward about 25° and northward about 15°, and then gradually moved back after the year 2009; (3) during the past 16 years, 0°C isotherm of China’s surface water has gradually moved north by about 0.09° in the latitude direction and east by about 1° in the longitude direction; (4) the inter-annual variation of water surface temperature in 17 lakes of China showed a similar fluctuation trend that increased before 2010, and then decreased. The El Niño and La Niña around 2010 could have impacts on the turning point of the annual variation of water surface temperature. This study validated the response of China’s inland surface water to global climate change and improved the understanding of the wetland environment’s response to climate change.

scholarly journals Future Hydrological Drought Risk Assessment Based on Nonstationary Joint Drought Management Index

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 532 ◽  
Author(s):  
Jisoo Yu ◽  
Tae-Woong Kim ◽  
Dong-Hyeok Park
Keyword(s):  
Climate Change ◽  
Generalized Additive Models ◽  
Hydrological Drought ◽  
Additive Models ◽  
Low Flow ◽  
Drought Risk ◽  
Climate Change Scenarios ◽  
Drought Management ◽  
Copula Models ◽  
Daily Streamflow

As the environment changes, the stationarity assumption in hydrological analysis has become questionable. If nonstationarity of an observed time series is not fully considered when handling climate change scenarios, the outcomes of statistical analyses would be invalid in practice. This study established bivariate time-varying copula models for risk analysis based on the generalized additive models in location, scale, and shape (GAMLSS) theory to develop the nonstationary joint drought management index (JDMI). Two kinds of daily streamflow data from the Soyang River basin were used; one is that observed during 1976–2005, and the other is that simulated for the period 2011–2099 from 26 climate change scenarios. The JDMI quantified the multi-index of reliability and vulnerability of hydrological drought, both of which cause damage to the hydrosystem. Hydrological drought was defined as the low-flow events that occur when streamflow is equal to or less than Q80 calculated from observed data, allowing future drought risk to be assessed and compared with the past. Then, reliability and vulnerability were estimated based on the duration and magnitude of the events, respectively. As a result, the JDMI provided the expected duration and magnitude quantities of drought or water deficit.

scholarly journals Anthropogenic refugia ameliorate the severe climate-related decline of a montane mammal along its trailing edge

2012 ◽  
Vol 279 (1745) ◽  
pp. 4279-4286 ◽  
Author(s):  
Toni Lyn Morelli ◽  
Adam B. Smith ◽  
Christina R. Kastely ◽  
Ilaria Mastroserio ◽  
Craig Moritz ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Use Change ◽  
Generalized Additive Models ◽  
Climate Change Impacts ◽  
Additive Models ◽  
Trailing Edge ◽  
Human Land Use ◽  
Precipitation And Temperature ◽  
Impacts Of Climate Change

We conducted detailed resurveys of a montane mammal, Urocitellus beldingi , to examine the effects of climate change on persistence along the trailing edge of its range. Of 74 California sites where U. beldingi were historically recorded (1902–1966), 42 per cent were extirpated, with no evidence for colonization of previously unoccupied sites. Increases in both precipitation and temperature predicted site extirpations, potentially owing to snowcover loss. Surprisingly, human land-use change buffered climate change impacts, leading to increased persistence and abundance. Excluding human-modified sites, U. beldingi has shown an upslope range retraction of 255 m. Generalized additive models of past distribution were predictive of modern range contractions (AUC = 0.76) and projected extreme reductions (52% and 99%, respectively) of U. beldingi's southwestern range to 2080 climates (Hadley and CCCMA A2). Our study suggests the strong impacts of climate change on montane species at their trailing edge and how anthropogenic refugia may mitigate these effects.

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