scholarly journals Methods for Exploring Spatial and Temporal Variability of Extreme Events in Climate Data

2008 ◽  
Vol 21 (10) ◽  
pp. 2072-2092 ◽  
Author(s):  
C. A. S. Coelho ◽  
C. A. T. Ferro ◽  
D. B. Stephenson ◽  
D. J. Steinskog
Keyword(s):  
Value Theory ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Variability ◽  
Spatial Dependency ◽  
Climate Data ◽  
Explanatory Factors ◽  
Generalized Pareto ◽  
Pareto Model ◽  
Grid Points ◽  
Extremal Properties

Abstract This study presents various statistical methods for exploring and summarizing spatial extremal properties in large gridpoint datasets. Extremal properties are inferred from the subset of gridpoint values that exceed sufficiently high, time-varying thresholds. A simple approach is presented for how to choose the thresholds so as to avoid sampling biases from nonstationary differential trends within the annual cycle. The excesses are summarized by estimating parameters of a flexible generalized Pareto model that can account for spatial and temporal variation in the excess distributions. The effect of potentially explanatory factors (e.g., ENSO) on the distribution of extremes can be easily investigated using this model. Smooth spatially pooled estimates are obtained by fitting the model over neighboring grid points while accounting for possible spatial variation across these points. Extreme value theory methods are also presented for how to investigate the temporal clustering and spatial dependency (teleconnections) of extremes. The methods are illustrated using Northern Hemisphere monthly mean gridded temperatures for June–August (JJA) summers from 1870 to 2005.

scholarly journals The Gamma Generalized Pareto Distribution with Applications in Survival Analysis

2017 ◽  
Vol 6 (3) ◽  
pp. 141 ◽  
Author(s):  
Thiago A. N. De Andrade ◽  
Luz Milena Zea Fernandez ◽  
Frank Gomes-Silva ◽  
Gauss M. Cordeiro
Keyword(s):  
Monte Carlo Simulation ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Real Data ◽  
Model Parameters ◽  
Monte Carlo Simulation Study ◽  
Lorenz Curves ◽  
Generalized Pareto ◽  
Mathematical Properties ◽  
Pareto Model

We study a three-parameter model named the gamma generalized Pareto distribution. This distribution extends the generalized Pareto model, which has many applications in areas such as insurance, reliability, finance and many others. We derive some of its characterizations and mathematical properties including explicit expressions for the density and quantile functions, ordinary and incomplete moments, mean deviations, Bonferroni and Lorenz curves, generating function, R\'enyi entropy and order statistics. We discuss the estimation of the model parameters by maximum likelihood. A small Monte Carlo simulation study and two applications to real data are presented. We hope that this distribution may be useful for modeling survival and reliability data.

scholarly journals Spatial and temporal variation of methane emissions in drained eutrophic peat agro-ecosystems: drainage ditches as emission hotspots

2008 ◽  
Vol 5 (2) ◽  
pp. 1237-1261 ◽  
Author(s):  
A. P. Schrier-Uijl ◽  
E. M. Veenendaal ◽  
P. A. Leffelaar ◽  
J. C. van Huissteden ◽  
F. Berendse
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Table ◽  
Soil Moisture Content ◽  
Explanatory Variable ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Variability ◽  
Ch4 Emissions ◽  
Intensively Managed ◽  
Moisture Conditions

Abstract. Our research investigates the spatial and temporal variability of methane (CH4) emissions in two drained eutrophic peat areas (one intensively managed and the other less intensively managed) and the correlation between CH4 emissions and soil temperature, air temperature, soil moisture content and water table. We stratified the landscape into landscape elements that represent different conditions in terms of topography and therefore differ in moisture conditions. There was great spatial variability in the fluxes in both areas; the ditches and ditch edges (together 27% of the landscape) were methane hotspots whereas the dry fields had the smallest fluxes. In the intensively managed site the fluxes were significantly higher by comparison with the less intensively managed site. In all the landscape element elements the best explanatory variable for CH4 emission was temperature. Neither soil moisture content nor water table correlated significantly with CH4 emissions, except in April, where soil moisture was the best explanatory variable.

A Python-based GIS Simulation of the Spatial and Temporal Variation in Evapotranspiration

2019 ◽  
Vol 35 (5) ◽  
pp. 759-765 ◽  
Author(s):  
Mohammed G. Mohammed ◽  
Kathleen M. Trauth
Keyword(s):  
Overland Flow ◽  
Potential Evapotranspiration ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Variability ◽  
Water Losses ◽  
Python Script ◽  
Space And Time ◽  
Land Covers ◽  
A Site ◽  
Linn County

Abstract. An assessment of potential evapotranspiration (ET) and direct evaporation is important for informed land management from agriculture to wetlands restoration. These processes vary in space and time, depending on vegetation, soils, and climate throughout the year. Much data has been collected in order to quantify ET for individual plots of land, but means have not been available to provide an integrated view on a landscape scale. A methodology has been developed and an implementing Python script has been written to assess and display the spatial and temporal variability of ET and direct evaporation using a geographic information system (GIS). The methodology utilizes publicly available inputs for broad applicability, and the calculations can be performed for a site with multiple land covers and soil textures. In addition to a visual representation of ET and direct evaporation in space and time, the Python script produces a text file of water losses that could be used in water balance calculations also incorporating precipitation, overland flow and infiltration. The methodology has been demonstrated on a site within Pershing State Park in Linn County, Missouri, and produces results consistent with those expected from hand calculations. All data and code are available in GitHub (https://github.com/TrauthK/Wetlands). Keywords: Evapotranspiration, Evaporation, GIS simulation, Hydrologic modeling, Hydrologic cycle, Python, Raster data, Wetland restoration.

scholarly journals Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory

1997 ◽  
Vol 27 (1) ◽  
pp. 117-137 ◽  
Author(s):  
Alexander J. McNeil
Keyword(s):  
Extreme Value Theory ◽  
Generalized Pareto Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Parametric Curve ◽  
Excess Loss ◽  
Generalized Pareto ◽  
Fire Insurance ◽  
Loss Severity ◽  
Parametric Curve Fitting

AbstractGood estimates for the tails of loss severity distributions are essential for pricing or positioning high-excess loss layers in reinsurance. We describe parametric curve-fitting methods for modelling extreme historical losses. These methods revolve around the generalized Pareto distribution and are supported by extreme value theory. We summarize relevant theoretical results and provide an extensive example of their application to Danish data on large fire insurance losses.

scholarly journals Glacier changes and climate trends derived from multiple sources in the data scarce Cordillera Vilcanota region, southern Peruvian Andes

2013 ◽  
Vol 7 (1) ◽  
pp. 103-118 ◽  
Author(s):  
N. Salzmann ◽  
C. Huggel ◽  
M. Rohrer ◽  
W. Silverio ◽  
B. G. Mark ◽  
...  
Keyword(s):  
Air Temperature ◽  
Specific Humidity ◽  
Spatial And Temporal Variability ◽  
Moderate Increase ◽  
Climate Data ◽  
Climate Trends ◽  
Multiple Sources ◽  
Mountain Areas ◽  
Peruvian Andes ◽  
Glacier Changes

Abstract. The role of glaciers as temporal water reservoirs is particularly pronounced in the (outer) tropics because of the very distinct wet/dry seasons. Rapid glacier retreat caused by climatic changes is thus a major concern, and decision makers demand urgently for regional/local glacier evolution trends, ice mass estimates and runoff assessments. However, in remote mountain areas, spatial and temporal data coverage is typically very scarce and this is further complicated by a high spatial and temporal variability in regions with complex topography. Here, we present an approach on how to deal with these constraints. For the Cordillera Vilcanota (southern Peruvian Andes), which is the second largest glacierized cordillera in Peru (after the Cordillera Blanca) and also comprises the Quelccaya Ice Cap, we assimilate a comprehensive multi-decadal collection of available glacier and climate data from multiple sources (satellite images, meteorological station data and climate reanalysis), and analyze them for respective changes in glacier area and volume and related trends in air temperature, precipitation and in a more general manner for specific humidity. While we found only marginal glacier changes between 1962 and 1985, there has been a massive ice loss since 1985 (about 30% of area and about 45% of volume). These high numbers corroborate studies from other glacierized cordilleras in Peru. The climate data show overall a moderate increase in air temperature, mostly weak and not significant trends for precipitation sums and probably cannot in full explain the observed substantial ice loss. Therefore, the likely increase of specific humidity in the upper troposphere, where the glaciers are located, is further discussed and we conclude that it played a major role in the observed massive ice loss of the Cordillera Vilcanota over the past decades.

Modeling the Seasonality of Extreme Waves in the Gulf of Mexico

2010 ◽  
Vol 133 (2) ◽  
Author(s):  
Philip Jonathan ◽  
Kevin Ewans
Keyword(s):  
Gulf Of Mexico ◽  
Wave Height ◽  
Significant Wave Height ◽  
Poisson Model ◽  
Extreme Value ◽  
Significant Wave ◽  
Generalized Pareto ◽  
Seasonal Model ◽  
Pareto Model ◽  
Optimal Value

Statistics of storm peaks over threshold depend typically on a number of covariates including location, season, and storm direction. Here, a nonhomogeneous Poisson model is adopted to characterize storm peak events with respect to season for two Gulf of Mexico locations. The behavior of storm peak significant wave height over threshold is characterized using a generalized Pareto model, the parameters of which vary smoothly with season using a Fourier form. The rate of occurrence of storm peaks is also modeled using a Poisson model with rate varying with season. A seasonally varying extreme value threshold is estimated independently. The degree of smoothness of extreme value shape and scale and the Poisson rate with season are regulated by roughness-penalized maximum likelihood; the optimal value of roughness is selected by cross validation. Despite the fact that only the peak significant wave height event for each storm is used for modeling, the influence of the whole period of a storm on design extremes for any seasonal interval is modeled using the concept of storm dissipation, providing a consistent means to estimate design criteria for arbitrary seasonal intervals. The characteristics of the 100 year storm peak significant wave height, estimated using the seasonal model, are examined and compared with those estimated ignoring seasonality.

scholarly journals Spatial and Temporal Variation in an Apple Orchard

HortScience ◽  
2019 ◽  
Vol 54 (12) ◽  
pp. 2182-2187
Author(s):  
Babak Talebpour ◽  
Maksut Barış Eminoğlu ◽  
Uğur Yegül ◽  
Ufuk Türker
Keyword(s):  
Temporal Variation ◽  
Precision Agriculture ◽  
Temporal Stability ◽  
Apple Orchard ◽  
The Other ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Variability ◽  
Research Station ◽  
Temporal Variance ◽  
Over Time

One important goal of precision horticulture (PH), as well as precision agriculture (PA), is to measure and manage spatial and temporal variation in orchards. In this study, temporal and spatial analysis of yields were carried out over 2 years for a 0.5-ha apple orchard (at the Haymana Research Station of Ankara University, Turkey, from 2017 to 2018) to determine the variability of yields over time and included seven apple varieties: ‘Royal Gala’, ‘Red Chief’, ‘Braeburn’, ‘Mondial Gala’, ‘Jonagold’, ‘Fuji’, and ‘Mitch Gala’. To achieve this, yield data for two different years were analyzed for mean yield, temporal variance, and cv in terms of spatial and temporal stability, and their yield maps were produced. The results showed that ‘Jonagold’, ‘Braeburn’, and ‘Red Chief’ varieties yielded less than the average yield, whereas the other varieties produced average yields when the yield from 2 years was taken into account. Calculation of the values for determining temporal stability over time resulted in all existing varieties being identified as stable over time. For example, the ‘Jonagold’ and ‘Red Chief’ varieties showed 100% stability in terms of temporal variance. Results also showed that the ‘Gala’ varieties were stable for 2 years and produced high yields, whereas the other varieties were specified as stable and low yielding when spatial and temporal variability was considered in combination.

scholarly journals Generalized Inference on Stress-Strength Reliability in Generalized Pareto Model

2021 ◽  
Author(s):  
Sanju Scaria ◽  
Seemon Thomas ◽  
Sibil Jose
Keyword(s):  
Simulation Studies ◽  
Strength Data ◽  
Percentile Method ◽  
Generalized Pareto ◽  
Variable Approach ◽  
Pareto Model ◽  
Coverage Probabilities ◽  
Generalized Inference ◽  
Bootstrap Percentile ◽  
Generalized Variable Approach

The article focuses on the inference of stress-strength reliability in generalized Pareto model using the generalized variable approach and bootstrap percentile method. Simulation studies are conducted to obtain expected lengths and coverage probabilities of confidence intervals constructed using the generalized variable and the bootstrap percentile methods. An example consisting of real stress-strength data is also presented for illustrative purposes.

scholarly journals Spatio-Temporal Variability of Phytoplankton Primary Production in Baltic Lakes Using Sentinel-3 OLCI Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2415
Author(s):  
Tuuli Soomets ◽  
Kristi Uudeberg ◽  
Kersti Kangro ◽  
Dainis Jakovels ◽  
Agris Brauns ◽  
...  
Keyword(s):  
Primary Production ◽  
Temporal Variability ◽  
Carbon Fixation ◽  
Remote Sensing Data ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Variability ◽  
Phytoplankton Primary Production ◽  
Spatio Temporal ◽  
Lake Size

Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be 117 million lakes). In this study, bio-optical modelling and remote sensing data (Sentinel-3 Ocean and Land Colour Instrument) was combined to investigate the spatial and temporal variation of PP in four Baltic lakes during 2018. The model used has three input parameters: concentration of chlorophyll-a, the diffuse attenuation coefficient, and incident downwelling irradiance. The largest of our studied lakes, Võrtsjärv (270 km2), had the highest total yearly estimated production (61 Gg C y−1) compared to the smaller lakes Lubans (18 Gg C y−1) and Razna (7 Gg C y−1). However, the most productive was the smallest studied, Lake Burtnieks (40.2 km2); although the total yearly production was 13 Gg C y−1, the daily average areal production was 910 mg C m−2 d−1 in 2018. Even if lake size plays a significant role in the total PP of the lake, the abundance of small and medium-sized lakes would sum up to a significant contribution of carbon fixation. Our method is applicable to larger regions to monitor the spatial and temporal variability of lake PP.

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