Seventy-seven years of natural disturbances in a mountain forest area — the influence of storm, snow, and insect damage analysed with a long-term time series

2008 ◽  
Vol 38 (8) ◽  
pp. 2249-2261 ◽  
Author(s):  
Marc Hanewinkel ◽  
Johannes Breidenbach ◽  
Till Neeff ◽  
Edgar Kublin
Keyword(s):  
Time Series ◽  
Forest Structure ◽  
Forest Damage ◽  
Mountain Forest ◽  
Natural Disturbances ◽  
Storm Events ◽  
Insect Outbreaks ◽  
Planted Forests ◽  
Risk Processes

We investigated the effects of site properties, forest structure, and time on snow breakage, insect outbreaks, windthrow, and total damage for predominantly planted forests. A time series of forest damage in southwestern Germany spanning 77 years, from 1925 to 2001, was available along with a database on site properties and forest structure. The statistical modeling procedure successively addressed (i) probability of damage occurrence, (ii) timber loss in damaging events, and (iii) interaction among damage agents over time. Logistic and linear regressions were combined with multivariate autoregressive techniques. Natural disturbances were responsible for a total timber loss of 3.0 m3· ha–1· year–1. The distribution of the timber loss values over the years and over sites and stands with different properties was modeled with a standard error of 6.7 m3· ha–1· year–1. Disturbances are more likely to occur in previously damaged stands. Storm events typically provoke subsequent insect outbreaks between 2 and 6 years later. Large windthrow and snow breakage events tend to occur periodically, once every 10th, 11th, or 15th year. Analysis of disturbances as a time series significantly enhances understanding of forest risk processes.

scholarly journals Assessing dry weather flow contribution in TSS and COD storm events loads in combined sewer systems

2011 ◽  
Vol 63 (12) ◽  
pp. 2983-2991 ◽  
Author(s):  
M. Métadier ◽  
J. L. Bertrand-Krajewski
Keyword(s):  
Time Series ◽  
Oxygen Demand ◽  
Seasonal Effect ◽  
Storm Events ◽  
Combined System ◽  
Time Step ◽  
Urban Catchment ◽  
Sewer Systems ◽  
Continuous Discharge

Continuous high resolution long term turbidity measurements along with continuous discharge measurements are now recognised as an appropriate technique for the estimation of in sewer total suspended solids (TSS) and Chemical Oxygen Demand (COD) loads during storm events. In the combined system of the Ecully urban catchment (Lyon, France), this technique is implemented since 2003, with more than 200 storm events monitored. This paper presents a method for the estimation of the dry weather (DW) contribution to measured total TSS and COD event loads with special attention devoted to uncertainties assessment. The method accounts for the dynamics of both discharge and turbidity time series at two minutes time step. The study is based on 180 DW days monitored in 2007–2008. Three distinct classes of DW days were evidenced. Variability analysis and quantification showed that no seasonal effect and no trend over the year were detectable. The law of propagation of uncertainties is applicable for uncertainties estimation. The method has then been applied to all measured storm events. This study confirms the interest of long term continuous discharge and turbidity time series in sewer systems, especially in the perspective of wet weather quality modelling.

Modelling of overflow emissions in flanders

1998 ◽  
Vol 38 (10) ◽  
pp. 41-48 ◽  
Author(s):  
G. Vaes ◽  
J. Berlamont
Keyword(s):  
Time Series ◽  
Storm Events ◽  
Short Time Series ◽  
Hydrodynamic Simulations ◽  
Sewer Systems ◽  
Linear Behaviour ◽  
Long Time ◽  
Short Time ◽  
Different Characteristics

Ideally, for emission calculations long term hydrodynamic simulations should be performed, but this requires long calculation times. Simplifications are consequently necessary. Due to the non-linear behaviour of sewer systems, hydrodynamic simulations using single storm events often will not lead to a good probability estimation of the overflow emissions. Simplified models using long time simulations give better results if they are well calibrated. To increase the accuracy hydrodynamic simulations with short time series can be used. The short time series are selected from the long time historical rainfall series using a simplified model. To test the accuracy of these three methods, hydrodynamic long term simulations were performed for several (small) sewer systems with different characteristics to compare with.

Mapping forest natural disturbances dynamics in the Aosta Valley (Italy) through long-term trends derived from Landsat time series and innovative statistical approaches

2020 ◽  
Author(s):  
Raffaella Marzano ◽  
Donato Morresi ◽  
Emanuele Lingua ◽  
Renzo Motta ◽  
Matteo Garbarino
Keyword(s):  
Time Series ◽  
Forest Dynamics ◽  
Vegetation Indices ◽  
Airborne Lidar ◽  
Natural Disturbances ◽  
Spectral Indices ◽  
Long Term Trends ◽  
One Year ◽  
Normalized Burn Ratio

<p>Forest dynamics triggered by natural disturbances occurred in the Aosta Valley region were spatially mapped over time using long-term trends derived from Landsat time series spanning over 35 years, from 1985 to 2019. Among biotic and abiotic disturbance agents, the following were selected: wildfires, windthrows, snow avalanches, landslides and insect outbreaks. Landsat TM, ETM+ and OLI images acquired during the vegetative season (from June to September) with less than 80% cloud cover were employed to create synthetic images at one-year interval using the geometric median approach at the pixel-level. Forest dynamics due to disturbance occurrence and the following vegetation recovery were explored through inter-annual time series of different spectral indices such as normalized vegetation indices (Normalized Burn Ratio, Normalized Moisture Index) and the tasseled cap band transformations (wetness, angle). Changes in the linear trends of the spectral indices time series caused by disturbance occurrence were detected using a novel bottom-up approach in which a wavelet basis is adaptively constructed by merging neighbouring segments of the data. This method doesn’t require a priori knowledge of the time series parameters making it fully automated. Prior to perform the trend analysis, vegetation indices time series were filtered to remove residual invalid pixels and fill gaps of one-year length. Considering abrupt disturbances, this method highlighted sensitivity toward both high and low magnitude events and was able to accurately detect different severity degrees within the perimeter of the affected forest area. Historical wildfire perimeters and crown fires patches provided by the forest fire fighting corps of the Aosta Valley were used to perform preliminary severity maps validation. Considering two severity classes, ‘low to moderate’ and ‘moderate to high’, maps produced using the Normalized Burn Ratio achieved an overall accuracy of 83%. Future work is aimed to validate all the selected natural disturbance agents using historical field data available at the regional scale. Moreover, a rigorous and wide scale-based assessment of the capabilities of the algorithm in tracking post-fire forest recovery will be performed by integrating forest structure data from filed surveys and airborne LiDAR measurements.</p>

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Observations of Tidal Flat Sedimentation within a Native and an Exotic Spartina Species

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1566
Author(s):  
Barbara Proença ◽  
Florian Ganthy ◽  
Richard Michalet ◽  
Aldo Sottolichio
Keyword(s):  
Single Point ◽  
Field Measurements ◽  
Species Variation ◽  
Storm Events ◽  
Level Variation ◽  
Wave Forcing ◽  
Bed Elevation ◽  
Sediment Stabilization ◽  
Consistent Response

Field measurements of bed elevation and related wave events were performed within a tidal marsh, on two cordgrass species, Spartina anglica (exotic) and Spartina maritima (native), in the Bay of Arcachon (SW France). Bed- and water-level time series were used to infer on the sediment behavior patterns from short to long term. A consistent response was found between the bed-level variation and the wave forcing, with erosion occurring during storms and accretion during low energy periods. Such behavior was observed within the two species, but the magnitude of bed-level variation was higher within the native than the exotic Spartina. These differences, in the order of millimeters, were explained by the opposite allocation of biomass of the two species. On the long term, the sedimentation/erosion patterns were dominated by episodic storm events. A general sediment deficit was observed on the site, suggested by an overall bed-level decrease registered within both species. However, further verification of within species variation needs to be considered when drawing conclusions. Despite possible qualitative limitations of the experimental design, due to single point survey, this work provides original and considerable field data to the understanding the different species ability to influence bed sediment stabilization and their potential to build marsh from the mudflat pioneer stage. Such information is valuable for coastal management in the context of global change.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

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.

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