Long-term trends in seasonal plankton dynamics in Lake Mead (Nevada-Arizona, USA) and implications for climate change

Hydrobiologia ◽  
2018 ◽  
Vol 822 (1) ◽  
pp. 85-109 ◽  
Author(s):  
John R. Beaver ◽  
Janet E. Kirsch ◽  
Claudia E. Tausz ◽  
Erin E. Samples ◽  
Thomas R. Renicker ◽  
...  
Keyword(s):  
Climate Change ◽  
Lake Mead ◽  
Plankton Dynamics ◽  
Long Term Trends

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

scholarly journals Forest insects and climate change: long-term trends in herbivore damage

2013 ◽  
Vol 3 (12) ◽  
pp. 4183-4196 ◽  
Author(s):  
Maartje J. Klapwijk ◽  
György Csóka ◽  
Anikó Hirka ◽  
Christer Björkman
Keyword(s):  
Climate Change ◽  
Herbivore Damage ◽  
Forest Insects ◽  
Long Term Trends

scholarly journals Examining the Climatology of Shortwave Radiation in the Northeastern United States

2017 ◽  
Vol 56 (10) ◽  
pp. 2869-2881
Author(s):  
Janel Hanrahan ◽  
Alexandria Maynard ◽  
Sarah Y. Murphy ◽  
Colton Zercher ◽  
Allison Fitzpatrick
Keyword(s):  
Climate Change ◽  
United States ◽  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Predictor Variable ◽  
Shortwave Radiation ◽  
Northeastern United States ◽  
Weather Patterns ◽  
Long Term Trends

AbstractAs demand for renewable energy grows, so does the need for an improved understanding of renewable energy sources. Paradoxically, the climate change mitigation strategy of fossil fuel divestment is in itself subject to shifts in weather patterns resulting from climate change. This is particularly true with solar power, which depends on local cloud cover. However, because observed shortwave radiation data usually span a decade or less, persistent long-term trends may not be identified. A simple linear regression model is created here using diurnal temperature range (DTR) during 2002–15 as a predictor variable to estimate long-term shortwave radiation (SR) values in the northeastern United States. Using an extended DTR dataset, SR values are computed for 1956–2015. Statistically significant decreases in shortwave radiation are identified that are dominated by changes during the summer months. Because this coincides with the season of greatest insolation and the highest potential for energy production, financial implications may be large for the solar energy industry if such trends persist into the future.

scholarly journals Analysis of Long-Term Trends of Annual and Seasonal Rainfall in the Awash River Basin, Ethiopia

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1498 ◽  
Author(s):  
Solomon Mulugeta ◽  
Clifford Fedler ◽  
Mekonen Ayana
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Winter Rainfall ◽  
Adaptation Measures ◽  
Awash River Basin ◽  
Long Term Trends ◽  
Increasing Trends

With climate change prevailing around the world, understanding the changes in long-term annual and seasonal rainfall at local scales is very important in planning for required adaptation measures. This is especially true for areas such as the Awash River basin where there is very high dependence on rain- fed agriculture characterized by frequent droughts and subsequent famines. The aim of the study is to analyze long-term trends of annual and seasonal rainfall in the Awash River Basin, Ethiopia. Monthly rainfall data extracted from Climatic Research Unit (CRU 4.01) dataset for 54 grid points representing the entire basin were aggregated to find the respective areal annual and seasonal rainfall time series for the entire basin and its seven sub-basins. The Mann-Kendall (MK) test and Sen Slope estimator were applied to the time series for detecting the trends and for estimating the rate of change, respectively. The Statistical software package R version 3.5.2 was used for data extraction, data analyses, and plotting. Geographic information system (GIS) package was also used for grid making, site selection, and mapping. The results showed that no significant trend (at α = 0.05) was identified in annual rainfall in all sub-basins and over the entire basin in the period (1902 to 2016). However, the results for seasonal rainfall are mixed across the study areas. The summer rainfall (June through September) showed significant decreasing trend (at α ≤ 0.1) over five of the seven sub-basins at a rate varying from 4 to 7.4 mm per decade but it showed no trend over the two sub-basins. The autumn rainfall (October through January) showed no significant trends over four of the seven sub-basins but showed increasing trends over three sub-basins at a rate varying from 2 to 5 mm per decade. The winter rainfall (February through May) showed no significant trends over four sub-basins but showed significant increasing trends (at α ≤ 0.1) over three sub-basins at a rate varying from 0.6 to 2.7 mm per decade. At the basin level, the summer rainfall showed a significant decreasing trend (at α = 0.05) while the autumn and winter rainfall showed no significant trends. In addition, shift in some amount of summer rainfall to winter and autumn season was noticed. It is evident that climate change has shown pronounced effects on the trends and patterns of seasonal rainfall. Thus, the study contribute to better understanding of climate change in the basin and the information from the study can be used in planning for adaptation measures against a changing climate.

Long-term trends in ocean chlorophyll: update from a Bayesian hierarchical space-time model

2020 ◽  
Author(s):  
Claudie Beaulieu ◽  
Matthew Hammond ◽  
Stephanie Henson ◽  
Sujit Sahu
Keyword(s):  
Climate Change ◽  
Coupled Model ◽  
Climate Change Impacts ◽  
Space Time ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Color Sensors ◽  
Long Term Trends ◽  
Space Time Model

<p>Assessing ongoing changes in marine primary productivity is essential to determine the impacts of climate change on marine ecosystems and fisheries. Satellite ocean color sensors provide detailed coverage of ocean chlorophyll in space and time, now with a combined record length of just over 20 years. Detecting climate change impacts is hindered by the shortness of the record and the long timescale of memory within the ocean such that even the sign of change in ocean chlorophyll is still inconclusive from time-series analysis of satellite data. Here we use a Bayesian hierarchical space-time model to estimate long-term trends in ocean chlorophyll. The main advantage of this approach comes from the principle of ”borrowing strength” from neighboring grid cells in a given region to improve overall detection. We use coupled model simulations from the CMIP5 experiment to form priors to provide a “first guess” on observational trend estimates and their uncertainty that we then update using satellite observations. We compare the results with estimates obtained with the commonly used vague prior, reflecting the case where no independent knowledge is available.  A global average net positive chlorophyll trend is found, with stronger regional trends that are typically positive in high and mid latitudes, and negative at low latitudes outside the Atlantic. The Bayesian hierarchical model used here provides a framework for integrating different sources of data for detecting trends and estimating their uncertainty in studies of global change.</p>

Threats to the running water ecosystems of the world

2002 ◽  
Vol 29 (2) ◽  
pp. 134-153 ◽  
Author(s):  
Björn Malmqvist ◽  
Simon Rundle
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Developed Countries ◽  
Running Waters ◽  
Running Water ◽  
Lotic Systems ◽  
The World ◽  
Long Term Trends ◽  
Water Ecosystems

Running waters are perhaps the most impacted ecosystem on the planet as they have been the focus for human settlement and are heavily exploited for water supplies, irrigation, electricity generation, and waste disposal. Lotic systems also have an intimate contact with their catchments and so land-use alterations affect them directly. Here long-term trends in the factors that currently impact running waters are reviewed with the aim of predicting what the main threats to rivers will be in the year 2025. The main ultimate factors forcing change in running waters (ecosystem destruction, physical habitat and water chemistry alteration, and the direct addition or removal of species) stem from proximate influences from urbanization, industry, land-use change and water-course alterations. Any one river is likely to be subjected to several types of impact, and the management of impacts on lotic systems is complicated by numerous links between different forms of anthropogenic effect. Long-term trends for different impacts vary. Concentrations of chemical pollutants such as toxins and nutrients have increased in rivers in developed countries over the past century, with recent reductions for some pollutants (e.g. metals, organic toxicants, acidification), and continued increases in others (e.g. nutrients); there are no long-term chemical data for developing countries. Dam construction increased rapidly during the twentieth century, peaking in the 1970s, and the number of reservoirs has stabilized since this time, whereas the transfer of exotic species between lotic systems continues to increase. Hence, there have been some success stories in the attempts to reduce the impacts from anthropogenic impacts in developed nations. Improvements in the pH status of running waters should continue with lower sulphurous emissions, although emissions of nitrous oxides are set to continue under current legislation and will continue to contribute to acidification and nutrient loadings. Climate change also will impact running waters through alterations in hydrology and thermal regimes, although precise predictions are problematic; effects are likely to vary between regions and to operate alongside rather than override those from other impacts. Effects from climate change may be more extreme over longer time scales (>50 years). The overriding pressure on running water ecosystems up to 2025 will stem from the predicted increase in the human population, with concomitant increases in urban development, industry, agricultural activities and water abstraction, diversion and damming. Future degradation could be substantial and rapid (c. 10 years) and will be concentrated in those areas of the world where resources for conservation are most limited and knowledge of lotic ecosystems most incomplete; damage will centre on lowland rivers, which are also relatively poorly studied. Changes in management practices and public awareness do appear to be benefiting running water ecosystems in developed countries, and could underpin conservation strategies in developing countries if they were implemented in a relevant way.

scholarly journals Climate Change and Future Long-Term Trends of Rainfall at North-East of Iraq

2014 ◽  
Vol 8 (6) ◽  
Author(s):  
Nadhir Al-Ansari ◽  
Mawada Abdellatif ◽  
Mohammad Ezeelden ◽  
Salahalddin S. Ali ◽  
Sven Knutsson
Keyword(s):  
Climate Change ◽  
North East ◽  
Long Term Trends

scholarly journals Vertical Differences in the Long-Term Trends and Breakpoints of NDVI and Climate Factors in Taiwan

2021 ◽  
Vol 13 (22) ◽  
pp. 4707
Author(s):  
Hui Ping Tsai ◽  
Geng-Gui Wang ◽  
Zhong-Han Zhuang
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Positive Effects ◽  
Central Regions ◽  
Long Term Trends

This study explored the long-term trends and breakpoints of vegetation, rainfall, and temperature in Taiwan from overall and regional perspectives in terms of vertical differences from 1982 to 2012. With time-series Advanced Very-High-Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data and Taiwan Climate Change Estimate and Information Platform (TCCIP) gridded monthly climatic data, their vertical dynamics were investigated by employing the Breaks for Additive Seasonal and Trend (BFAST) algorithm, Pearson’s correlation analysis, and the Durbin–Watson test. The vertical differences in NDVI values presented three breakpoints and a consistent trend from positive (1982 to 1989) to negative at varied rates, and then gradually increased after 2000. In addition, a positive rainfall trend was discovered. Average and maximum temperature had similar increasing trends, while minimum temperature showed variations, especially at higher altitudes. In terms of regional variations, the vegetation growth was stable in the north but worse in the central region. Higher elevations revealed larger variations in the NDVI and temperature datasets. NDVI, along with average and minimum temperature, showed their largest changes earlier in higher altitude areas. Specifically, the increasing minimum temperature direction was more prominent in the mid-to-high-altitude areas in the eastern and central regions. Seasonal variations were observed for each region. The difference between the dry and wet seasons is becoming larger, with the smallest difference in the northern region and the largest difference in the southern region. Taiwan’s NDVI and climatic factors have a significant negative correlation (p < 0.05), but the maximum and minimum temperatures have significant positive effects at low altitudes below 500 m. The northern and central regions reveal similar responses, while the south and east display different feedbacks. The results illuminate climate change evidence from assessment of the long-term dynamics of vegetation and climatic factors, providing valuable references for establishing correspondent climate-adaptive strategies in Taiwan.

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