scholarly journals Correlated Functional Models with Derivative Information for Modeling Microfading Spectrometry Data on Rock Art Paintings

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2141
Author(s):  
Gabriel Riutort-Mayol ◽  
Virgilio Gómez-Rubio ◽  
José Luis Lerma ◽  
Julio M. del Hoyo-Meléndez
Keyword(s):  
Time Series ◽  
Rock Art ◽  
High Sensitivity ◽  
Modeling Framework ◽  
Spatially Correlated ◽  
Functional Models ◽  
Color Fading ◽  
Spatio Temporal ◽  
Derivative Information

Rock art paintings present high sensitivity to light, and an exhaustive evaluation of the potential color degradation effects is essential for further conservation and preservation actions on these rock art systems. Microfading spectrometry (MFS) is a technique that provides time series of stochastic observations that represent color fading over time at the measured points on the surface under study. In this work, a reliable and robust modeling framework for a short and greatly fluctuating observation dataset collected over the surfaces of rock art paintings located on the walls of Cova Remigia in Ares del Maestrat, Castellón, Spain, is presented. The model is based on a spatially correlated spline-based time series model that takes into account prior information in the form of model derivatives to guarantee monotonicity and long-term saturation for predictions of new color fading estimates at unobserved locations on the surface. The correlation among the (spatially located) time series is modeled by defining Gaussian process (GP) priors over the spline coefficients across time series. The goal is to obtain a complete spatio-temporal mapping of color fading estimates for the study area, which results in very important and useful information that will potentially serve to create better policies and guidelines for heritage preservation and sustainable rock art cultural tourism.

scholarly journals CONSTRUCTION AND ANALYSIS OF LONG-TERM SURFACE TEMPERATURE DATASET IN FUJIAN PROVINCE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1223-1227 ◽  
Author(s):  
W. E. Li ◽  
X. Q. Wang ◽  
H. Su
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface ◽  
Rapid Development ◽  
Temporal Distribution ◽  
Heat Fluxes ◽  
Urban Region ◽  
Fujian Province ◽  
Spatio Temporal

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.

scholarly journals Simulating the effects of weather and climate on large wildfires in France

2018 ◽  
Author(s):  
Renaud Barbero ◽  
Thomas Curt ◽  
Anne Ganteaume ◽  
Eric Maillé ◽  
Marielle Jappiot ◽  
...  
Keyword(s):  
Linear Models ◽  
Weather Conditions ◽  
Historical Period ◽  
Natural Ecosystem ◽  
Atmospheric Variability ◽  
Modeling Framework ◽  
Weather And Climate ◽  
Change Context ◽  
Spatio Temporal

Abstract. Large wildfires across parts of France can cause devastating damages which put lives, infrastructures, and natural ecosystem at risk. One of the most challenging questions in the climate change context is how these large wildfires relate to weather and climate and how they might change in a warmer world. Such projections rely on the development of a robust modeling framework linking wildfires to atmospheric variability. Drawing from a MODIS product and a gridded meteorological dataset, we derived a suite of biophysical and fire danger indices and developed generalized linear models simulating the probability of large wildfires (> 100 ha) at 8-km spatial and daily temporal resolutions across the entire country over the MODIS period. The models were skillful in reproducing the main spatio-temporal patterns of large wildfires across different environmental regions. Long-term drought was found to be a significant predictor of large wildfires in flammability-limited systems such as the Alpine and Southwest regions. In the Mediterranean, large wildfires were found to be associated with both short-term fire weather conditions and longer-term soil moisture deficits, collectively facilitating the occurrence of large wildfires. Simulated probabilities during the day of large wildfires were on average 2–3 times higher than normal with respect to the mean seasonal cycle. The model has wide applications, including improving our understanding of the drivers of large wildfires over the historical period and providing a basis to estimate future changes to large wildfire from climate scenarios.

scholarly journals Precipitation isotope time series predictions from machine learning applied in Europe

2021 ◽  
Vol 118 (26) ◽  
pp. e2024107118
Author(s):  
Daniel B. Nelson ◽  
David Basler ◽  
Ansgar Kahmen
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Climate Data ◽  
Modeling Framework ◽  
History Of ◽  
Oxygen Isotopic ◽  
Input Variables ◽  
Hydrogen And Oxygen Isotope ◽  
High Level

Hydrogen and oxygen isotope values of precipitation are critically important quantities for applications in Earth, environmental, and biological sciences. However, direct measurements are not available at every location and time, and existing precipitation isotope models are often not sufficiently accurate for examining features such as long-term trends or interannual variability. This can limit applications that seek to use these values to identify the source history of water or to understand the hydrological or meteorological processes that determine these values. We developed a framework using machine learning to calculate isotope time series at monthly resolution using available climate and location data in order to improve precipitation isotope model predictions. Predictions from this model are currently available for any location in Europe for the past 70 y (1950–2019), which is the period for which all climate data used as predictor variables are available. This approach facilitates simple, user-friendly predictions of precipitation isotope time series that can be generated on demand and are accurate enough to be used for exploration of interannual and long-term variability in both hydrogen and oxygen isotopic systems. These predictions provide important isotope input variables for ecological and hydrological applications, as well as powerful targets for paleoclimate proxy calibration, and they can serve as resources for probing historic patterns in the isotopic composition of precipitation with a high level of meteorological accuracy. Predictions from our modeling framework, Piso.AI, are available at https://isotope.bot.unibas.ch/PisoAI/.

scholarly journals Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

2020 ◽  
Author(s):  
Francois Rerolle ◽  
Emily Dantzer ◽  
Andrew A. Lover ◽  
John M. Marshall ◽  
Bouasy Hongvanthong ◽  
...  
Keyword(s):  
Forest Cover ◽  
Malaria Incidence ◽  
Lao Pdr ◽  
Short Term ◽  
Modeling Framework ◽  
Temporal Modeling ◽  
Forested Areas ◽  
Spatio Temporal ◽  
The Relationship

AbstractAs countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on forest-going populations, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases in confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest-going populations on malaria transmission in the GMS.

scholarly journals Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Francois Rerolle ◽  
Emily Dantzer ◽  
Andrew A Lover ◽  
John M Marshall ◽  
Bouasy Hongvanthong ◽  
...  
Keyword(s):  
Forest Cover ◽  
Malaria Incidence ◽  
Lao Pdr ◽  
Short Term ◽  
Modeling Framework ◽  
Temporal Modeling ◽  
Forested Areas ◽  
Spatio Temporal ◽  
The Relationship

As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on reducing forest-related transmission, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest activities on malaria transmission in the GMS.

scholarly journals Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM model

2015 ◽  
Vol 19 (6) ◽  
pp. 2717-2736 ◽  
Author(s):  
A. Kuentz ◽  
T. Mathevet ◽  
J. Gailhard ◽  
B. Hingray
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Air Temperature ◽  
Regional Scale ◽  
Precipitation Time Series ◽  
Temporal Scales ◽  
Spatial Homogeneity ◽  
Temperature And Precipitation ◽  
Spatio Temporal

Abstract. Efforts to improve the understanding of past climatic or hydrologic variability have received a great deal of attention in various fields of geosciences such as glaciology, dendrochronology, sedimentology and hydrology. Based on different proxies, each research community produces different kinds of climatic or hydrologic reanalyses at different spatio-temporal scales and resolutions. When considering climate or hydrology, many studies have been devoted to characterising variability, trends or breaks using observed time series representing different regions or climates of the world. However, in hydrology, these studies have usually been limited to short temporal scales (mainly a few decades and more rarely a century) because they require observed time series (which suffer from a limited spatio-temporal density). This paper introduces ANATEM, a method that combines local observations and large-scale climatic information (such as the 20CR Reanalysis) to build long-term probabilistic air temperature and precipitation time series with a high spatio-temporal resolution (1 day and a few km2). ANATEM was tested on the reconstruction of air temperature and precipitation time series of 22 watersheds situated in the Durance River basin, in the French Alps. Based on a multi-criteria and multi-scale diagnosis, the results show that ANATEM improves the performance of classical statistical models – especially concerning spatial homogeneity – while providing an original representation of uncertainties which are conditioned by atmospheric circulation patterns. The ANATEM model has been also evaluated for the regional scale against independent long-term time series and was able to capture regional low-frequency variability over more than a century (1883–2010).

scholarly journals Four Decades of Estuarine Wetland Changes in the Yellow River Delta Based on Landsat Observations Between 1973 and 2013

Water ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 933 ◽  
Author(s):  
Changming Zhu ◽  
Xin Zhang ◽  
Qiaohua Huang
Keyword(s):  
Time Series ◽  
Yellow River ◽  
Yellow River Delta ◽  
River Delta ◽  
Evolution Process ◽  
The Yellow River ◽  
The Yellow River Delta ◽  
The Past ◽  
Spatio Temporal

Yellow River Delta wetlands are essential for the migration of endangered birds and breeding. The wetlands, however, have been severely damaged during recent decades, partly due to the lack of wetland ecosystem protection by authorities. To have a better historical understanding of the spatio-temporal dynamics of the wetlands, this study aims to map and characterize patterns of the loss and degradation of wetlands in the Yellow River Delta using a time series of remotely sensed images (at nine points in time) based on object-based image analysis and knowledge transfer learning technology. Spatio-temporal analysis was conducted to document the long-term changes taking place in different wetlands over the four decades. The results showed that the Yellow River Delta wetlands have experienced significant changes between 1973 and 2013. The total area of wetlands has been reduced by 683.12 km2 during the overall period and the trend of loss continues. However, the rates and trends of change for the different types of wetlands were not the same. The natural wetlands showed a statistically significant decrease in area during the overall period (36.04 km2·year−1). Meanwhile, the artificial wetlands had the opposite trend and showed a statistically significant increase in area during the past four decades (18.96 km2·year−1). According to the change characteristics revealed by the time series wetland classification maps, the evolution process of the Yellow River Delta wetlands could be divided into three stages: (1) From 1973–1984, basically stable, but with little increase; (2) from 1984–1995, rapid loss; and (3) from 1995–2013, slow loss. The area of the wetlands reached a low point around 1995, and then with a little improvement, the regional wetlands entered a slow loss stage. It is believed that interference by human activities (e.g., urban construction, cropland creation, and oil exploitation) was the main reason for wetland degradation in the Yellow River Delta over the past four decades. Climate change also has long-term impacts on regional wetlands. In addition, due to the special geographical environment, the hydrological and sediment conditions and the location of the Yellow River mouth also have a significant influence on the evolution process of the wetlands.

scholarly journals Long-term meteorological and hydrological dryness and wetness conditions in the Zhujiang River Basin, South China

2012 ◽  
Vol 9 (9) ◽  
pp. 10525-10562
Author(s):  
T. Fischer ◽  
M. Gemmer ◽  
B. Su ◽  
T. Scholten
Keyword(s):  
Time Series ◽  
River Basin ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Economic Losses ◽  
Xijiang River ◽  
The Xijiang River ◽  
Spatio Temporal ◽  
The Standardized Precipitation Index

Abstract. Floods and droughts are frequently causing large economic losses in China. These conditions vary in space, time, and magnitude. In this study, long-term meteorological and hydrological dryness and wetness conditions are analyzed for the Xijiang River Basin which is the largest tributary of the Zhujiang (Pearl) River. A very similar inter-annual course of precipitation and discharge can be observed. The standardized precipitation index (SPI) is used to show dryness and wetness pattern in the six sub-basins of the Xijiang River. The SPI-24 correlates high with the standardized discharge index (SDI-24) for Gaoyao hydrological station at the mouth of Xijiang River. Distinct long-term dryness and wetness sequences are found in the time series for the SPI-24 and SDI-24. The principal component analysis reveals many spatial interdependencies in dryness and wetness conditions for the sub-basins and explains some spatio-temporal disparities. Moderate dryness conditions have a larger spatial impact than moderate wetness conditions in the sub-basins. The loading pattern of the first principal component shows that the correlation with the entire Xijiang River Basin is highest in the eastern and lowest in the western sub-basins. Further spatial dipole conditions explain the spatio-temporal heterogeneity of dryness and wetness conditions. Accordingly, the precipitation in the eastern sub-basins contributes more to the hydrological wetness conditions than in the western sub-basins, which mainly contribute to dryness patterns. The spectral analysis for the SPI-24 (entire Xijiang River Basin) and SDI-24 shows similar peaks for periods of 11–14.7 yr, 2.8 yr, 3.4–3.7 yr, and 6.3–7.3 yr. The same periods can be found for the SPI-24 of Xijiang River's six sub-basins with some variability in the magnitude. The wavelet analysis shows that the most significant periods are stable over time since the 1980s. The extrapolations of the reconstructed time series do not suggest any spatial or temporal changes in the occurrence of dryness and wetness conditions in the next two decades but a continuation of the observed cycles at given magnitude. It can be concluded that long-term hydrological dryness and wetness conditions are directly caused by periodic cycles of meteorological conditions (i.e. precipitation). The applied methodologies prove to be able to identify spatial interdependencies and corresponding regional disparities, and to detect significant periodicities in long-term dryness and wetness conditions in the Xijiang River Basin.

scholarly journals Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM method

2015 ◽  
Vol 12 (1) ◽  
pp. 311-361 ◽  
Author(s):  
A. Kuentz ◽  
T. Mathevet ◽  
J. Gailhard ◽  
B. Hingray
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Air Temperature ◽  
Large Scale ◽  
Precipitation Time Series ◽  
Time Step ◽  
Spatial Homogeneity ◽  
Temperature And Precipitation ◽  
Spatio Temporal

Abstract. Improving the understanding of past climatic or hydrologic variability has received a large attention in different fields of geosciences, such as glaciology, dendrochronology, sedimentology or hydrology. Based on different proxies, each research community produces different kind of climatic or hydrologic reanalyses, at different spatio-temporal scales and resolution. When considering climate or hydrology, numerous studies aim at characterising variability, trends or breaks using observed time-series of different regions or climate of world. However, in hydrology, these studies are usually limited to reduced temporal scale (mainly few decades, seldomly a century) because they are limited to observed time-series, that suffers from a limited spatio-temporal density. This paper introduces a new model, ANATEM, based on a combination of local observations and large scale climatic informations (such as 20CR Reanalysis). This model allow to build long-term air temperature and precipitation time-series, with a high spatio-temporal resolution (daily time-step, few km2). ANATEM was tested on the air temperature and precipitation time-series of 22 watersheds situated on the Durance watershed, in the french Alps. Based on a multi-criteria and multi-scale diagnostic, the results show that ANATEM improves the performances of classical statistical models. ANATEM model have been validated on a regional level, improving spatial homogeneity of performances and on independent long-term time-series, being able to capture the regional low-frequency variabilities over more than a century (1883–2010).

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