scholarly journals A worldwide analysis of trends in water-balance evapotranspiration

2013 ◽  
Vol 17 (10) ◽  
pp. 4177-4187 ◽  
Author(s):  
A. M. Ukkola ◽  
I. C. Prentice
Keyword(s):  
Water Balance ◽  
Interannual Variability ◽  
Land Surface ◽  
Time Series Data ◽  
Hydrological Cycle ◽  
Atmospheric Co2 ◽  
Series Data ◽  
Data Sets ◽  
Terrestrial Water ◽  
Strong Control

Abstract. Climate change is expected to alter the global hydrological cycle, with inevitable consequences for freshwater availability to people and ecosystems. But the attribution of recent trends in the terrestrial water balance remains disputed. This study attempts to account statistically for both trends and interannual variability in water-balance evapotranspiration (ET), estimated from the annual observed streamflow in 109 river basins during "water years" 1961–1999 and two gridded precipitation data sets. The basins were chosen based on the availability of streamflow time-series data in the Dai et al. (2009) synthesis. They were divided into water-limited "dry" and energy-limited "wet" basins following the Budyko framework. We investigated the potential roles of precipitation, aerosol-corrected solar radiation, land use change, wind speed, air temperature, and atmospheric CO2. Both trends and variability in ET show strong control by precipitation. There is some additional control of ET trends by vegetation processes, but little evidence for control by other factors. Interannual variability in ET was overwhelmingly dominated by precipitation, which accounted on average for 54–55% of the variation in wet basins (ranging from 0 to 100%) and 94–95% in dry basins (ranging from 69 to 100%). Precipitation accounted for 45–46% of ET trends in wet basins and 80–84% in dry basins. Net atmospheric CO2 effects on transpiration, estimated using the Land-surface Processes and eXchanges (LPX) model, did not contribute to observed trends in ET because declining stomatal conductance was counteracted by slightly but significantly increasing foliage cover.

scholarly journals A worldwide analysis of trends in water-balance evapotranspiration

2013 ◽  
Vol 10 (5) ◽  
pp. 5739-5765 ◽  
Author(s):  
A. M. Ukkola ◽  
I. C. Prentice
Keyword(s):  
Water Balance ◽  
Interannual Variability ◽  
Land Surface ◽  
Time Series Data ◽  
Hydrological Cycle ◽  
Atmospheric Co2 ◽  
Series Data ◽  
Additional Control ◽  
Terrestrial Water ◽  
Strong Control

Abstract. Climate change is expected to alter the global hydrological cycle, with inevitable consequences for freshwater availability to people and ecosystems. But the attribution of recent trends in the terrestrial water balance remains disputed. This study attempts to account statistically for both trends and interannual variability in water-balance evapotranspiration (ET), estimated from the annual observed streamflow in 109 river basins during "water years" 1961–1999 and two gridded precipitation datasets. The basins were chosen based on the availability of streamflow time-series data in the Dai et al. (2009) synthesis. They were divided into water-limited "dry" and energy-limited "wet" basins following the Budyko framework. We investigated the potential roles of precipitation, aerosol-corrected solar radiation, land-use change, wind speed, air temperature, and atmospheric CO2. Both trends and variability in ET show strong control by precipitation. There is some additional control of ET trends by vegetation processes, but little evidence for control by other factors. Interannual variability in ET was overwhelmingly dominated by precipitation, which accounted on average for 52–54% of the variation in wet basins (ranging from 0 to 99%) and 84–85% in dry basins (ranging from 13 to 100%). Precipitation accounted for 39–42% of ET trends in wet basins and 69–79% in dry basins. Cropland expansion increased ET in dry basins. Net atmospheric CO2 effects on transpiration, estimated using the Land-surface Processes and eXchanges (LPX) model, did not contribute to observed trends in ET because declining stomatal conductance was counteracted by slightly but significantly increasing foliage cover.

scholarly journals Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

2017 ◽  
Author(s):  
Anthony Szedlak ◽  
Spencer Sims ◽  
Nicholas Smith ◽  
Giovanni Paternostro ◽  
Carlo Piermarocchi
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Cell Cycle ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Human Cervical Cancer

AbstractModern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.Author SummaryCell cycle – the process in which a parent cell replicates its DNA and divides into two daughter cells – is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

An Efficient Method for Forecasting Using Fuzzy Time Series

2017 ◽  
pp. 287-304 ◽  
Author(s):  
Pritpal Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Data Sets ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

scholarly journals Growth Limits: A Conceptual Analysis for Sustainable Development in Nigeria

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402091827
Author(s):  
Oluwabunmi O. Adejumo
Keyword(s):  
Time Series Data ◽  
Optimal Growth ◽  
Series Data ◽  
Data Sets ◽  
African Countries ◽  
Limits To Growth ◽  
Development Indicators ◽  
Long Run ◽  
Sub Saharan

In the school of development thought, growth has been identified as a viable alternative to the challenge of poverty and economic backwardness. However, the ecologists have continuously challenged the growth position in relation to environmental degradation and depletion. It is against this background; this study examined the limits to growth in Nigeria beyond which there will be inimical consequences for the environment. The study employed time series data that spanned between 1970 and 2014. These data sets were sourced from the World Development Indicators. Based on the assimilation model, threshold estimates were used to identify optimal growth regions, whereas regression estimates were used to measure growth effects. It was discovered that below the identified growth limit, there are currently significant negative impacts on the quality of the environment in Nigeria via economic growth. This study is a single-country case, that is, Nigeria; hence, the study can be expanded to include other sub-Saharan African countries. The study adds to knowledge by establishing the prospects for sustainability in the quality of the environment in the long run; therefore, policies designed in this areas have higher likelihood of attaining sustainability.

scholarly journals Multimodel Estimate of the Global Terrestrial Water Balance: Setup and First Results

2011 ◽  
Vol 12 (5) ◽  
pp. 869-884 ◽  
Author(s):  
Ingjerd Haddeland ◽  
Douglas B. Clark ◽  
Wietse Franssen ◽  
Fulco Ludwig ◽  
Frank Voß ◽  
...  
Keyword(s):  
Water Balance ◽  
Land Surface ◽  
Water Model ◽  
Hydrological Models ◽  
Model Intercomparison ◽  
Land Surface Models ◽  
Surface Models ◽  
Terrestrial Water ◽  
Intercomparison Project ◽  
Energy Balance Approach

Abstract Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

scholarly journals AstroCatR: a mechanism and tool for efficient time series reconstruction of large-scale astronomical catalogues

2020 ◽  
Vol 496 (1) ◽  
pp. 629-637
Author(s):  
Ce Yu ◽  
Kun Li ◽  
Shanjiang Tang ◽  
Chao Sun ◽  
Bin Ma ◽  
...  
Keyword(s):  
Time Series ◽  
High Performance ◽  
Large Scale ◽  
Extrasolar Planets ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Observation Data ◽  
Data Volume ◽  
And Performance

ABSTRACT Time series data of celestial objects are commonly used to study valuable and unexpected objects such as extrasolar planets and supernova in time domain astronomy. Due to the rapid growth of data volume, traditional manual methods are becoming extremely hard and infeasible for continuously analysing accumulated observation data. To meet such demands, we designed and implemented a special tool named AstroCatR that can efficiently and flexibly reconstruct time series data from large-scale astronomical catalogues. AstroCatR can load original catalogue data from Flexible Image Transport System (FITS) files or data bases, match each item to determine which object it belongs to, and finally produce time series data sets. To support the high-performance parallel processing of large-scale data sets, AstroCatR uses the extract-transform-load (ETL) pre-processing module to create sky zone files and balance the workload. The matching module uses the overlapped indexing method and an in-memory reference table to improve accuracy and performance. The output of AstroCatR can be stored in CSV files or be transformed other into formats as needed. Simultaneously, the module-based software architecture ensures the flexibility and scalability of AstroCatR. We evaluated AstroCatR with actual observation data from The three Antarctic Survey Telescopes (AST3). The experiments demonstrate that AstroCatR can efficiently and flexibly reconstruct all time series data by setting relevant parameters and configuration files. Furthermore, the tool is approximately 3× faster than methods using relational data base management systems at matching massive catalogues.

Pentad Evolution of the 1988 Drought and 1993 Flood over the Great Plains: An NARR Perspective on the Atmospheric and Terrestrial Water Balance

2009 ◽  
Vol 22 (20) ◽  
pp. 5366-5384 ◽  
Author(s):  
Scott J. Weaver ◽  
Alfredo Ruiz-Barradas ◽  
Sumant Nigam
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Great Plains ◽  
Land Surface ◽  
Surface States ◽  
Moisture Flux ◽  
Summer Drought ◽  
Southern Plains ◽  
The North ◽  
Terrestrial Water

Abstract The evolution of the atmospheric and land surface states during extreme hydroclimate episodes over North America is investigated using the North American Regional Reanalysis (NARR), which additionally, and successfully, assimilates precipitation. The pentad-resolution portrayals of the atmospheric and terrestrial water balance over the U.S. Great Plains during the 1988 summer drought and the July 1993 floods are analyzed to provide insight into the operative mechanisms including regional circulation (e.g., the Great Plains low-level jet, or GPLLJ) and hydroclimate (e.g., precipitation, evaporation, soil moisture recharge, runoff). The submonthly (but supersynoptic time scale) fluctuations of the GPLLJ are found to be very influential, through related moisture transport and kinematic convergence (e.g., ∂υ/∂y), with the jet anomalies in the southern plains leading the northern precipitation and related moisture flux convergence, accounting for two-thirds of the dry and wet episode precipitation amplitude. The soil moisture influence on hydroclimate evolution is assessed to be marginal as evaporation anomalies are found to lag precipitation ones, a lead–lag not discernible at monthly resolution. The pentad analysis thus corroborates the authors’ earlier findings on the importance of transported moisture over local evaporation in Great Plains’ summer hydroclimate variability. The regional water budgets—atmospheric and terrestrial—are found to be substantially unbalanced, with the terrestrial imbalance being unacceptably large. Pentad analysis shows the atmospheric imbalance to arise from the sluggishness of the NARR evaporation, including its overestimation in wet periods. The larger terrestrial imbalance, on the other hand, has its origins in the striking unresponsiveness of the NARR’s runoff, which is underestimated in wet episodes. Finally, the influence of ENSO and North Atlantic Oscillation (NAO) variability on the GPLLJ is quantified during the wet episode, in view of the importance of moisture transports. It is shown that a significant portion (∼25%) of the GPLLJ anomaly (and downstream precipitation) is attributable to NAO and ENSO’s influence, and that this combined influence prolongs the wet episode beyond the period of the instigating GPLLJ.

FRACTAL ANALYSIS OF DIFFERENT EASTERN AND WESTERN MUSICAL INSTRUMENTS

Fractals ◽  
2006 ◽  
Vol 14 (03) ◽  
pp. 165-170 ◽  
Author(s):  
ATIN DAS ◽  
PRITHA DAS
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Fractal Dimension ◽  
Time Series Data ◽  
Musical Instruments ◽  
Series Data ◽  
Data Sets ◽  
Different Types ◽  
Frequency Regime ◽  
Musical Sounds

In this paper, we attempt musical analysis by measuring fractal dimension (D) of musical pieces played by several musical instruments. We collected solo performances of popular instruments of Western and Eastern origin as samples. We attempted usual spectral analysis of the selected clips to observe peaks of fundamental and harmonics in frequency regime. After appropriate processing, we converted them into time series data sets and computed their fractal dimension. Based on our results, we conclude that instrumental musical sounds may have higher Ds than those computed from vocal performances of different types of Indian songs.

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