Tomasz Niedzielski

Does Presence of a Mid-Ocean Ridge Enhance Biomass and Biodiversity?

In contrast to generally sparse biological communities in open-ocean settings, seamounts and ridges are perceived as areas of elevated productivity and biodiversity capable of supporting commercial fisheries. We investigated the origin of this apparent biological enhancement over a segment of the North Mid-Atlantic Ridge (MAR) using sonar, corers, trawls, traps, and a remotely operated vehicle to survey habitat, biomass, and biodiversity. Satellite remote sensing provided information on flow patterns, thermal fronts, and primary production, while sediment traps measured export flux during 2007–2010. The MAR, 3,704,404 km2 in area, accounts for 44.7% lower bathyal habitat (800–3500 m depth) in the North Atlantic and is dominated by fine soft sediment substrate (95% of area) on a series of flat terraces with intervening slopes either side of the ridge axis contributing to habitat heterogeneity. The MAR fauna comprises mainly species known from continental margins with no evidence of greater biodiversity. Primary production and export flux over the MAR were not enhanced compared with a nearby reference station over the Porcupine Abyssal Plain. Biomasses of benthic macrofauna and megafauna were similar to global averages at the same depths totalling an estimated 258.9 kt C over the entire lower bathyal north MAR. A hypothetical flat plain at 3500 m depth in place of the MAR would contain 85.6 kt C, implying an increase of 173.3 kt C attributable to the presence of the Ridge. This is approximately equal to 167 kt C of estimated pelagic biomass displaced by the volume of the MAR. There is no enhancement of biological productivity over the MAR; oceanic bathypelagic species are replaced by benthic fauna otherwise unable to survive in the mid ocean. We propose that globally sea floor elevation has no effect on deep sea biomass; pelagic plus benthic biomass is constant within a given surface productivity regime.

Automated system for near-real time modelling and prediction of altimeter-derived sea level anomalies

This paper serves as a presentation of a novel geoinformation system and a dedicated service, jointly named as Prognocean and based at the University of Wroclaw (Poland), that aim to predict Sea Level Anomaly (SLA) maps and publish them online. The system works in near-real time and is updated daily. The data are provided by the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO), and the time series processed by Prognocean is delivered by various altimetric satellites. The emphasis is put on gridded SLA maps, also known as MSLA, which are provided as Delayed Time (DT) and Near-Real Time (NRT) daily products. The daily sampling interval, however, does not coincide with typical repeat cycles of altimetric satellites and is obtained through reprocessing produced by AVISO. The two-module infrastructure forms the system. The first module is responsible for the near-real time communication with AVISO to download the most recent MSLA data and acquire the corrected data when the geophysical corrections have been available. The second module forms the main engine which does data processing, modelling, forecasting, statistical quality control and finally generates products as maps. The online service, however, publishes the products online every day. The above-mentioned components and infrastructure are described in detail. The performance of the system was evaluated using at least 150 predicted MSLA maps, available after half year of computations carried out in near-real time. We identified a few regions of imperfect performance of our prognoses and found that they spatially correspond to the mouth of the Amazon River and locations of key mesoscale eddies, the vast majority of which being nonlinear and hence unmodelled in our experiment.

A procedure for delineating a search region in the UAV-based SAR activities

ABSTRACT We propose a simple geometrical approach for delineating a region above which an Unmanned Aerial Vehicle (UAV) should fly to support the Search and Rescue (SAR) activities. The procedure is based on the concept of a crows flight distance travelled by a lost person and its probability distribution, for areas in which there does not exist any SAR database that can be used to estimate parameters of such a distribution. The novelty of the procedure lies in its indirect character, namely we do not estimate these parameters but we seek regions that reveal comparable topographic settings in order to borrow the parameters from where they are known. Our analysis focuses on the Wakeby probability distribution of the crows flight distance, the parameters of which are known for Alberta in Canada. We compare topographic and ecological characteristics of Alberta with the same features in Poland and argue that – under a few assumptions – it is allowed to use the Wakeby probabilistic model for the Canadian region in Polish conditions. Having borrowed the parameters in question, we present the skills of the geometrical approach in an experiment that utilizes flight simulations carried out with two professional micro UAV systems.

Non-Linear Sea Level Variations in the Eastern Tropical Pacific

Non-Linear Sea Level Variations in the Eastern Tropical Pacific The objective of this paper is to provide an insightful interpretation for the non-linearity of the inter-annual signal in sea level change in the eastern tropical Pacific. Such a non-linearity has been already discussed elsewhere for global ocean. Herein, the residual sea level anomaly time series from TOPEX/Poseidon and Jason-1 altimetry is obtained by removing the significant deterministic signals from the original sea level anomaly data. Since the eastern tropical Pacific is a profound region where many processes responsible for driving the El Niño/Southern Oscillation (ENSO) act, it is possible to link a few of them with the non-linearity of sea level change. In particular, not only local, usually weak, oceanatmosphere interactions exist in the eastern equatorial Pacific but this region is also remotely impacted by climatic processes acting in the western equatorial Pacific where the oceanatmosphere coupling is the strongest. The detected non-linearity of sea level change is due to the asymmetry between warm and cold ENSO episodes. Such an asymmetry can be driven by the non-linear dynamical heating associated with strong ENSO events.

Empirical Hydrologic Predictions for Southwestern Poland and Their Relation to Enso Teleconnections

Empirical Hydrologic Predictions for Southwestern Poland and Their Relation to Enso Teleconnections Recent investigations confirm meaningful but weak teleconnections between the El Niño/Southern Oscillation (ENSO) and hydrology in some European regions. In particular, this finding holds for Polish riverflows in winter and early spring as inferred from integrating numerous geodetic, geophysical and hydrologic time series. The purpose of this study is to examine whether such remote teleconnections may have an influence on hydrologic forecasting. The daily discharge time series from southwestern (SW) Poland spanning the time interval from 1971 to 2006 are examined. A few winter and spring peak flows are considered and the issue of their predictability using empirical forecasting is addressed. Following satisfactory prediction performance reported elsewhere, the multivariate autoregressive method is used and its modification based on the finite impulse response filtering is proposed. The initial phases of peak flows are rather acceptably forecasted but the accuracy of predictions in the vicinity of local maxima of the hydrographs is poorer. It has been hypothesized that ENSO signal slightly influences the predictability of winter and early spring floods in SW Poland. The predictions of flood wave maxima are the most accurate for floods preceded by normal states, less accurate for peak flows after La Niño episodes and highly inaccurate for peak flows preceded by El Niño events. Such a finding can be interpreted in terms of intermittency. Before peak flows preceded by El Niño there are temporarily persistent low flows followed by a consecutive melting leading to a considerable intermittency and hence to difficulties in forecasting. Before peak flows preceded by La Niño episodes there exist ENSO-related positive temperature and precipitation anomalies in SW Poland causing lower, but still considerable, intermittency and thus better, but not entirely correct, predictability of hydrologic time series.

An Application of Low-Order Arma and Garch Models for Sea Level Fluctuations

An Application of Low-Order Arma and Garch Models for Sea Level Fluctuations The paper presents the analysis of geographically-dependent irregular sea level fluctuations, often referred to as residual terms around deterministic signals, carried out by means of stochastic low-order autoregressive moving average (ARMA) and generalised autoregressive conditional heteroscedastic (GARCH) models. The gridded sea level anomaly (SLA) time series from TOPEX/Poseidon (T/P) and Jason-1 (J-1) satellite altimetry, commencing on 10th January 1993 and finishing on 14th July 2003, has been examined. The aforementioned models, limited to low-orders being combinations of 0,1 and 2, have been fitted to the SLA data. The root mean square and the Shapiro-Wilk test for the normal distribution have been used to calculate statistics of the residuals from these models. It has been found that autoregressive (AR) models as well as ARMA ones serve well the purpose of adequate modelling irregular sea level fluctuations, with a successful fit in some patchy bits of the equatorial Pacific. In contrast, GARCH models have been shown to be rather inaccurate, specifically in the vicinity of the tropical Pacific, in the North Pacific and in the equatorial Indian Ocean. The pattern of the Tropical Instability Waves (TIWs) has been noticed in the statistics of AR and ARMA model residuals indicating that the dynamics of these waves cannot be captured by the aforementioned linear stochastic processes.

The nested k‐means method: A new approach for detecting lost persons in aerial images acquired by unmanned aerial vehicles

A new method, named as the nested k-means, for detecting a person captured in aerial images acquired by an unmanned aerial vehicle (UAV), is presented. The nested k-means method is used in a newly built system that supports search and rescue (SAR) activities through processing of aerial photographs taken in visible light spectra (red-green-blue channels, RGB). First, the k-means classification is utilized to identify clusters of colors in a three-dimensional space (RGB). Second, the k-means method is used to verify if the automatically selected class of colors is concurrently spatially clustered in a two-dimensional space (easting-northing, EN), and has human-size area. The UAV images were acquired during the field campaign carried out in the Izerskie Mountains (SW Poland). The experiment aimed to observe several persons using an RGB camera, in spring and winter, during various periods of day, in uncovered terrain and sparse forest. It was found that the nested k-means method has a considerable potential for detecting a person lost in the wilderness and allows to reduce area to be searched to 4.4 and 7.3% in spring and winter, respectively. In winter, land cover influences the performance of the nested k-means method, with better skills in sparse forest than in the uncovered terrain. In spring, such a relationship does not hold. The nested k-means method may provide the SAR teams with a tool for near real-time detection of a person and, as a consequence, to reduce search area to approximately 0.5–7.3% of total terrain to be visited, depending on season and land cover.

Relation between design floods based on daily maxima and daily means: use of the Peak Over Threshold approach in the Upper Nysa Kłodzka Basin (SW Poland)

ABSTRACT The estimation of flood quantiles is crucial in the assessment of the magnitude and frequency of floods. We carried out a comparative analysis of design discharges estimated from both daily maximum flows and daily mean flows for four mountainous catchments located in the Upper Nysa Kłodzka river basin (SW Poland). After separation of baseflow, split of the riverflow time series in independent events, and selection of the Peak Over Threshold sample, the parameters of the Generalized Pareto Distribution were estimated using the Hill statistic, after bias correction, and considering asymptotic properties. The comparison was performed for various return periods, where the long return periods were of main concern. The jack-knife approach was used to assess the uncertainty of the predicted flood quantiles, and comparison was made with an alternative approach based on annual maxima. We found a meaningful level of differences between daily maximum and mean design discharges and between the rate of change of flood magnitude for which the level (i) stabilized with increasing return period, (ii) decreased downstream, and (iii) was large for catchments susceptible to flooding and with high elevation change. Results are useful in practice when daily maximum discharge is not routinely recorded.

Automated Snow Extent Mapping Based on Orthophoto Images from Unmanned Aerial Vehicles

The paper presents the application of the k-means clustering in the process of automated snow extent mapping using orthophoto images generated using the Structure-from-Motion (SfM) algorithm from oblique aerial photographs taken by unmanned aerial vehicle (UAV). A simple classification approach has been implemented to discriminate between snow-free and snow-covered terrain. The procedure uses the k-means clustering and classifies orthophoto images based on the three-dimensional space of red–green–blue (RGB) or near-infrared–red–green (NIRRG) or near-infrared–green–blue (NIRGB) bands. To test the method, several field experiments have been carried out, both in situations when snow cover was continuous and when it was patchy. The experiments have been conducted using three fixed-wing UAVs (swinglet CAM by senseFly, eBee by senseFly, and Birdie by FlyTech UAV) on 10/04/2015, 23/03/2016, and 16/03/2017 within three test sites in the Izerskie Mountains in southwestern Poland. The resulting snow extent maps, produced automatically using the classification method, have been validated against real snow extents delineated through a visual analysis and interpretation offered by human analysts. For the simplest classification setup, which assumes two classes in the k-means clustering, the extent of snow patches was estimated accurately, with areal underestimation of 4.6% (RGB) and overestimation of 5.5% (NIRGB). For continuous snow cover with sparse discontinuities at places where trees or bushes protruded from snow, the agreement between automatically produced snow extent maps and observations was better, i.e. 1.5% (underestimation with RGB) and 0.7–0.9% (overestimation, either with RGB or with NIRRG). Shadows on snow were found to be mainly responsible for the misclassification.

Can Clouds Improve the Performance of Automated Human Detection in Aerial Images

The objective of this paper is to investigate the role of clouds in the effectiveness of automated human detection in aerial imagery acquired by unmanned aerial vehicles (UAVs). The automated processing is carried out with the nested k-means method applied to images taken in poor visibility caused by low-altitude clouds. Data were acquired during a field experiment carried out in the Izerskie Mountains (southwestern Poland). The fixed-wing UAV took RGB aerial photographs of terrain where persons simulated being lost in the wilderness. The UAV flights were conducted in the morning and around the noon, when clouds reduced clarity of aerial images. Subsequent UAV missions were performed in the afternoon and in the evening, when clouds had no impact on imagery. False hit rates