scholarly journals Bayesian trend analysis of extreme wind using observed and hindcast series off the Catalan coast, NW Mediterranean Sea

2014 ◽  
Vol 14 (9) ◽  
pp. 2387-2397 ◽  
Author(s):  
M. I. Ortego ◽  
J. J. Egozcue ◽  
R. Tolosana-Delgado
Keyword(s):  
Generalized Pareto Distribution ◽  
Western Mediterranean ◽  
Quality Data ◽  
Occurrence Rate ◽  
Data Series ◽  
Model Parameters ◽  
Storm Events ◽  
Average Wind Speed ◽  
Magnitude Distribution ◽  
Catalan Coast

Abstract. It has been suggested that climate change might modify the occurrence rate and magnitude of large ocean-wave and wind storms. The hypothesised reason is the increase of available energy in the atmosphere–ocean system. Forecasting models are commonly used to assess these effects, given that good-quality data series are often too short. However, forecasting systems are often tuned to reproduce the average behaviour, and there are concerns on their relevance for extremal regimes. We present a methodology of simultaneous analysis of observed and hindcast data with the aim of extracting potential time drifts as well as systematic regime discrepancies between the two data sources. The method is based on the peak-over-threshold (POT) approach and the generalized Pareto distribution (GPD) within a Bayesian estimation framework. In this context, storm events are considered points in time, and modelled as a Poisson process. Storm magnitude over a reference threshold is modelled with a GPD, a flexible model that captures the tail behaviour of the magnitude distribution. All model parameters, i.e. shape and location of the magnitude GPD and the Poisson occurrence rate, are affected by a trend in time. Moreover, a systematic difference between parameters of hindcast and observed series is considered. Finally, the posterior joint distribution of all these trend parameters is studied using a conventional Gibbs sampler. This method is applied to compare hindcast and observed series of average wind speed at a deep buoy location off the Catalan coast (NE Spain, western Mediterranean; buoy data from 2001; REMO wind hindcasting from 1958 on). Appropriate scale and domain of attraction are discussed, and the reliability of trends in time is addressed.

scholarly journals Bayesian trend analysis of extreme wind using observed and hindcast series off Catalan coast, NW Mediterranean Sea

2014 ◽  
Vol 2 (1) ◽  
pp. 799-824 ◽  
Author(s):  
M. I. Ortego ◽  
J. J. Egozcue ◽  
R. Tolosana-Delgado
Keyword(s):  
Generalized Pareto Distribution ◽  
Western Mediterranean ◽  
Quality Data ◽  
Occurrence Rate ◽  
Data Series ◽  
Model Parameters ◽  
Storm Events ◽  
Average Wind Speed ◽  
Magnitude Distribution ◽  
Catalan Coast

Abstract. It has been suggested that climate change might modify the occurrence rate and magnitude of large ocean-wave and wind storms. The hypothesised reason is the increase of available energy in the atmosphere-ocean system. Forecasting models are commonly used to assess these effects, given that good quality data series are often too short. However, forecasting systems are often tuned to reproduce the average behavior, and there are concerns on their relevance for extremal regimes. We present a methodology of simultaneous analysis of observed and hindcasted data with the aim of extracting potential time drifts as well as systematic regime discrepancies between the two data sources. The method is based on the Peak-Over-Threshold (POT) approach and the Generalized Pareto Distribution (GPD) within a Bayesian estimation framework. In this context, storm events are considered points in time, and modelled as a Poisson process. Storm magnitude over a reference threshold is modelled with a GPD, a flexible model that captures the tail behaviour of the magnitude distribution. All model parameters, i.e. shape and location of the magnitude GPD and the Poisson occurrence rate, are affected by a trend in time. Moreover, a systematic difference between parameters of hindcasted and observed series is considered. Finally, the posterior joint distribution of all these trend parameters is studied using a conventional Gibbs sampler. This method is applied to compare hindcast and observed series of 10 min average wind speed at a deep buoy location off the Catalan coast (NE Spain, Western Mediterranean; buoy data from 2001; REMO wind hindcasting from 1958 on). Appropriate scale and domain of attraction are discussed, and the reliability of trends in time are addressed.

A modeling approach to simulate impact of climate change in lake water quality: phytoplankton growth rate assessment

1998 ◽  
Vol 37 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Hany Hassan ◽  
Keisuke Hanaki ◽  
Tomonori Matsuo
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Growth Rate ◽  
Dissolved Oxygen ◽  
Lake Water ◽  
Phytoplankton Growth ◽  
Quality Data ◽  
Model Parameters ◽  
Lake Water Quality ◽  
Phytoplankton Growth Rate

Global climate change induced by increased concentrations of greenhouse gases (especially CO2) is expected to include changes in precipitation, wind speed, incoming solar radiation, and air temperature. These major climate variables directly influence water quality in lakes by altering changes in flow and water temperature balance. High concentration of nutrient enrichment and expected variability of climate can lead to periodic phytoplankton blooms and an alteration of the neutral trophic balance. As a result, dissolved oxygen levels, with low concentrations, can fluctuate widely and algal productivity may reach critical levels. In this work, we will present: 1) recent results of GCMs climate scenarios downscaling project that was held at the University of Derby, UK.; 2) current/future comparative results of a new mathematical lake eutrophication model (LEM) in which output of phytoplankton growth rate and dissolved oxygen will be presented for Suwa lake in Japan as a case study. The model parameters were calibrated for the period of 1973–1983 and validated for the period of 1983–1993. Meterologic, hydrologic, and lake water quality data of 1990 were selected for the assessment analysis. Statistical relationships between seven daily meteorological time series and three airflow indices were used as a means for downscaling daily outputs of Hadley Centre Climate Model (HadCM2SUL) to the station sub-grid scale.

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 Impacts of Polarimetric Radar Observations on Hydrologic Simulation

2010 ◽  
Vol 11 (3) ◽  
pp. 781-796 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Scott E. Giangrande ◽  
Yang Hong ◽  
Zachary L. Flamig ◽  
Terry Schuur ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Model Parameters ◽  
Polarimetric Radar ◽  
Storm Events ◽  
Hydrologic Simulation ◽  
Radar Observations

Abstract Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary. To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.

scholarly journals The Origin of Sand and Its Colour on the South-Eastern Coast of Spain: Implications for Erosion Management

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 377 ◽  
Author(s):  
Asensio-Montesinos ◽  
Pranzini ◽  
Martínez-Martínez ◽  
Cinelli ◽  
Anfuso ◽  
...  
Keyword(s):  
Western Mediterranean ◽  
Mediterranean Coast ◽  
Eastern Coast ◽  
Storm Events ◽  
Colour Space ◽  
Source Areas ◽  
Beach Width ◽  
Severe Erosion ◽  
Colour Coordinates ◽  
Economic Function

Sand colour can give important information about mineral composition and, consequently, sediment source areas and input systems. Beach appearance, which is mostly linked to sand colour, has a relevant economic function in tourist areas. In this paper, the colour of 66 sand samples, collected along both natural and nourished beaches in the western Mediterranean coast of Spain, were assessed in CIEL*a*b* 1976 colour space. The obtained results showed relevant differences between natural and artificially nourished beaches. The colour of many nourished beaches generally differs from the native one because the origin of the injected sand is different. The native sand colour coordinates’ range is: L* (40.16–63.71); a* (−1.47–6.40); b* (7.48–18.06). On the contrary, for nourished beaches’ the colour range is: L* (47.66–70.75); a*(0.72‒5.16); b* (5.82–18.82). Impacts of beach nourishment on the native sand colour were studied at San Juan beach, the most popular one along the study area. Nourishment works were performed after severe erosion, usually linked to anthropic activities/structures and storm events, but also to increase beach width and hence benefit tourism.

scholarly journals OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

scholarly journals Prediction of Muddy Floods Using High-Resolution Radar Precipitation Forecasts and Physically-Based Erosion Modeling in Agricultural Landscapes

Geosciences ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 401
Author(s):  
Phoebe Hänsel ◽  
Stefan Langel ◽  
Marcus Schindewolf ◽  
Andreas Kaiser ◽  
Arno Buchholz ◽  
...  
Keyword(s):  
High Resolution ◽  
Interdisciplinary Approach ◽  
Agricultural Landscapes ◽  
Transport Infrastructure ◽  
Data Series ◽  
Storm Events ◽  
Erosion Modeling ◽  
Physically Based ◽  
High Resolution Radar ◽  
Forecast Modeling

The monitoring, modeling, and prediction of storm events and accompanying heavy rain is crucial for intensively used agricultural landscapes and its settlements and transport infrastructure. In Saxony, Germany, repeated and numerous storm events triggered muddy floods from arable fields in May 2016. They caused severe devastation to settlements and transport infrastructure. This interdisciplinary approach investigates three muddy floods, which developed on silty soils of loess origin tending to soil surface sealing. To achieve this, the study focuses on the test of a historical forecast modeling of three muddy floods in ungauged agricultural landscapes. Therefore, this approach firstly illustrates the reconstruction of the muddy floods, which was performed by high-resolution radar precipitation data, physically-based erosion modeling, and the qualitative validation by unmanned aerial vehicle-based orthophotos. Subsequently, historical radar precipitation forecasts served as input data for the physically-based erosion model to test the forecast modeling retrospectively. The model results indicate a possible warning for two of the three muddy floods. This method of a historical forecast modeling of muddy floods seems particularly promising. Naturally, the data series of three muddy floods should be extended to more reliable data and statistical statements. Finally, this approach assesses the feasibility of a real-time muddy flood early warning system in ungauged agricultural landscapes by high-resolution radar precipitation forecasts and physically-based erosion modeling.

scholarly journals Characterising phosphorus transfers in rural catchments using a continuous bank-side analyser

2007 ◽  
Vol 11 (1) ◽  
pp. 372-381 ◽  
Author(s):  
P. Jordan ◽  
A. Arnscheidt ◽  
H. McGrogan ◽  
S. McCormick
Keyword(s):  
Point Sources ◽  
Mitigation Measures ◽  
Data Series ◽  
Storm Events ◽  
Catchment Scale ◽  
Concentration Data ◽  
Monitoring Technology ◽  
Transfer Event ◽  
Discharge Data ◽  
Total Period

Abstract. A six-month series of high-resolution synchronous stream discharge and total phosphorus (TP) concentration data is presented from a 5 km2 agricultural catchment in the Lough Neagh basin, Northern Ireland. The data are hourly averages of 10-minute measurements using a new bankside, automatic, continuous monitoring technology. Three TP transfer "event-types" occur in this catchment: (1) chronic, storm independent transfers; (2) acute, storm dependent transfers; (3) acute, storm independent transfers. Event-type 2 transferred over 90% of the total 279 kg TP load in 39% of the total period; it corresponded to diffuse transfers from agricultural soils. Event-types 1 and 3, however, maintained the river in a highly eutrophic state between storm events and were characteristic of point source pollution, despite there being no major industrial or municipal point sources. Managing P transfers at the catchment scale requires a robust monitoring technology to differentiate between dynamic, multiple sources and associated event types and so enable a reliable assessment of the performance of mitigation measures, monitored at catchment outlets. The synchronous and continuous TP and discharge data series generated in this study demonstrate how this is possible.

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