A propensity index for surface runoff on a karst plateau

Abstract. Karst aquifers are an important water resource, but are particularly vulnerable to pollution due to the typically short residence times. As the rainwater runs off on the surface it may collect contamination from faeces and other sources, before infiltrating the surface. It is therefore important to understand the spatial distribution of the frequency of surface runoff in karst areas. This paper proposes a new field-mapping method for the ability of the landscape to produce and convey surface runoff. The mapping method is based on (i) prior spatial information (e.g. geological map, terrain model), (ii) a visual assessment from a distance at the landscape scale (e.g. traces of surface runoff) and (iii) local data collection in the field (e.g. soil moisture, grain size distribution). The focus on variables that can be assessed from a distance in the landscape makes the method suitable for mapping larger areas than traditional field mapping. The mapping method is developed and tested for the 60 km2 Hochschwab area in Austria. The field mapping is used to specify a surface runoff propensity index which is tested against the spatial distribution of observed sink holes in the area. The mapping indicates that, in the study region, runoff occurs much more frequently in the poorly karstified dolomitic areas than in the limestone areas that are highly karstified. On dolomites, low permeable soils or debris prevail, often resulting in a permanent surface drainage network. On karstified limestone, sometimes overlaid by debris, surface runoff only occurs through infiltration excess at high rainfall intensities. Overall the analyses suggest that the mapping method is suitable for efficiently and reliably identifying spatial patterns of the ability of the landscape to produce and convey surface runoff in karst areas.

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A propensity index for surface runoff on a karst plateau

10.5194/hess-2018-228 ◽

2018 ◽

Author(s):

Christian Reszler ◽

Jürgen Komma ◽

Hermann Stadler ◽

Elmar Strobl ◽

Günter Blöschl

Keyword(s):

Surface Runoff ◽

Spatial Information ◽

Visual Assessment ◽

Mapping Method ◽

Local Data ◽

Study Region ◽

Field Mapping ◽

Terrain Model ◽

Karst Areas ◽

Infiltration Excess

Abstract. Karst aquifers are an important water resource, but are particularly vulnerable to pollution due to the typically short residence times. As the rainwater runs off on the surface it may collect contamination from faeces and other sources, before infiltrating. It is therefore important to understand the frequency of surface runoff in Karst areas. This paper proposes a new field mapping method for the ability of the landscape to produce and convey surface runoff. The mapping method is based on (i) local data collection in the field (e.g. soil moisture, grain size distribution), (ii) a visual assessment from a distance at the landscape scale (e.g. traces of surface runoff) and (iii) prior spatial information (e.g. geological map, terrain model). The focus on variables that can be assessed from a distance in the landscape makes the method suitable for mapping larger areas than traditional field mapping. The mapping method is developed and tested for the 60 km2 Hochschwab area in Austria. The field mapping is used to specify a surface runoff propensity index which is tested against the spatial distribution of observed sink holes in the area. The mapping indicates that, in the study region, runoff occurs much more frequently in the poorly karstified dolomitic areas than in the limestone areas that are highly karstified. On dolomites, low permeable soils or debris prevail, often resulting in a permanent surface drainage network. On karstified limestone, sometimes overlaid by debris, surface runoff only occurs through infiltration excess at high rainfall intensities. Overall the analyses suggest that the mapping method is suitable for efficiently and reliably identifying spatial patterns of the ability of the landscape to produce and convey surface runoff in karst areas.

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The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030166 ◽

2021 ◽

Vol 10 (3) ◽

pp. 166

Author(s):

Hartmut Müller ◽

Marije Louwsma

Keyword(s):

European Union ◽

Spatial Distribution ◽

Spatial Information ◽

National Level ◽

Temporal Information ◽

The European Union ◽

Member States ◽

Heavy Burden ◽

Spatio Temporal

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.

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Seismic doublets and a complex seismic sequence controlled by the rotation of the Juan Fernández microplate

10.5194/egusphere-egu21-1119 ◽

2021 ◽

Author(s):

Simone Cesca ◽

Carla Valenzuela Malebrán ◽

José Ángel López-Comino ◽

Timothy Davis ◽

Carlos Tassara ◽

...

Keyword(s):

Source Parameters ◽

Strike Slip ◽

Focal Mechanisms ◽

Joint Analysis ◽

Seismic Sequence ◽

Local Data ◽

Study Region ◽

Coulomb Stress Changes ◽

Earthquake Source Parameters ◽

Stress Changes

<p> A complex seismic sequence took place in 2014 at the Juan Fern&#225;ndez microplate, a small microplate located between Pacific, Nazca and Antarctica plates. Despite the remoteness of the study region and the lack of local data, we were able to resolve earthquake source parameters and to reconstruct the complex seismic sequence, by using modern waveform-based seismological techniques. The sequence started with an exceptional Mw 7.1-6.7 thrust &#8211; strike slip earthquake doublet, the first subevent being the largest earthquake ever recorded in the region and one of the few rare thrust earthquakes in a region otherwise characterized by normal faulting and strike slip earthquakes. The joint analysis of seismicity and focal mechanisms suggest the activation of E-W and NE-SW faults or of an internal curved pseudofault, which is formed in response to the microplate rotation, with alternation of thrust and strike-slip earthquakes. Seismicity migrated Northward in its final phase, towards the microplate edge, where a second doublet with uneven focal mechanisms occurred. The sequence rupture kinematics is well explained by Coulomb stress changes imparted by the first subevent. Our analysis show that compressional stresses, which have been mapped at the northern boundary of the microplate, but never accompanied by large thrust earthquakes, can be accommodated by the rare occurrence of large, impulsive, shallow thrust earthquakes, with a considerable tsunamigenic potential.</p>

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Spatial odor discrimination in hawkmoth, Manduca sexta (L.)

10.1101/2020.07.08.194126 ◽

2020 ◽

Author(s):

P. Kalyanasundaram ◽

M. A. Willis

Keyword(s):

Spatial Distribution ◽

Manduca Sexta ◽

Flight Control ◽

Spatial Information ◽

Egg Laying ◽

Proboscis Extension Reflex ◽

Flying Insects ◽

Odor Learning ◽

Odor Plumes ◽

Proboscis Extension

AbstractFlying insects track turbulent odor plumes to find mates, food and egg-laying sites. To maintain contact with the plume, insects are thought to adapt their flight control according to the distribution of odor in the plume using the timing of odor onsets and intervals between odor encounters. Although timing cues are important, few studies have addressed whether insects are capable of deriving spatial information about odor distribution from bilateral comparisons between their antennae in flight. The proboscis extension reflex (PER) associative learning protocol, originally developed to study odor learning in honeybees, was modified to show hawkmoths, Manduca sexta, can discriminate between odor stimuli arriving on either antenna. We show moths discriminated the odor arrival side with an accuracy of >70%. The information about spatial distribution of odor stimuli is thus available to moths searching for odor sources, opening the possibility that they use both spatial and temporal odor information.

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Study on the effect of rainfall spatial variability on runoff modelling

Journal of Hydroinformatics ◽

10.2166/hydro.2018.129 ◽

2018 ◽

Vol 20 (3) ◽

pp. 577-587 ◽

Cited By ~ 1

Author(s):

Jun Zhang ◽

Dawei Han ◽

Yang Song ◽

Qiang Dai

Keyword(s):

Spatial Distribution ◽

Spatial Variability ◽

Spatial Information ◽

Model Performance ◽

Moran’S I ◽

Image Size ◽

Moran's I ◽

Rainfall Events ◽

Lumped Model ◽

Distributed Models

Abstract Rainfall spatial variability was assessed to explore its influence on runoff modelling. Image size, coefficient of variation (Cv) and Moran's I were chosen to assess for rainfall spatial variability. The smaller the image size after compression, the less complex is the rainfall spatial variability. The results showed that due to the drawing procedure and varied compression methods, a large uncertainty exists for using image size to describe rainfall spatial variability. Cv quantifies the variability between different rainfall values without considering rainfall spatial distribution and Moran's I describes the spatial autocorrelation between gauges rather than the values. As both rainfall values and spatial distribution have an influence on runoff modelling, the combination of Cv and Moran's I was further explored. The results showed that the combination of Cv and Moran's I is reliable to describe rainfall spatial variability. Furthermore, with the increase of rainfall spatial variability, the hydrological model performance decreases. Moreover, it is difficult for a lumped model to cope with rainfall events assigned with complex rainfall spatial variability since spatial information is not taken into consideration (i.e. the VIC model used in this study). Therefore, it is recommended to apply distributed models that can deal with more spatial input information.

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Sensitivity of point scale surface runoff predictions to rainfall resolution

Hydrology and Earth System Sciences ◽

10.5194/hess-11-965-2007 ◽

2007 ◽

Vol 11 (2) ◽

pp. 965-982 ◽

Cited By ~ 12

Author(s):

A. J. Hearman ◽

C. Hinz

Keyword(s):

High Resolution ◽

Western Australia ◽

Surface Runoff ◽

Rainfall Data ◽

Soil Water Storage ◽

Point Scale ◽

Deep Drainage ◽

Model Predictions ◽

Infiltration Excess ◽

Saturation Excess

Abstract. This paper investigates the effects of using non-linear, high resolution rainfall, compared to time averaged rainfall on the triggering of hydrologic thresholds and therefore model predictions of infiltration excess and saturation excess runoff at the point scale. The bounded random cascade model, parameterized to three locations in Western Australia, was used to scale rainfall intensities at various time resolutions ranging from 1.875 min to 2 h. A one dimensional, conceptual rainfall partitioning model was used that instantaneously partitioned water into infiltration excess, infiltration, storage, deep drainage, saturation excess and surface runoff, where the fluxes into and out of the soil store were controlled by thresholds. The results of the numerical modelling were scaled by relating soil infiltration properties to soil draining properties, and in turn, relating these to average storm intensities. For all soil types, we related maximum infiltration capacities to average storm intensities (k*) and were able to show where model predictions of infiltration excess were most sensitive to rainfall resolution (ln k*=0.4) and where using time averaged rainfall data can lead to an under prediction of infiltration excess and an over prediction of the amount of water entering the soil (ln k*>2) for all three rainfall locations tested. For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating drainage coefficients to average storm intensities (g*) and parameter ranges where predicted runoff was dominated by infiltration excess or saturation excess depending on the resolution of rainfall data were determined (ln g*<2). Infiltration excess predicted from high resolution rainfall was short and intense, whereas saturation excess produced from low resolution rainfall was more constant and less intense. This has important implications for the accuracy of current hydrological models that use time averaged rainfall under these soil and rainfall conditions and predictions of larger scale phenomena such as hillslope runoff and runon. It offers insight into how rainfall resolution can affect predicted amounts of water entering the soil and thus soil water storage and drainage, possibly changing our understanding of the ecological functioning of the system or predictions of agri-chemical leaching. The application of this sensitivity analysis to different rainfall regions in Western Australia showed that locations in the tropics with higher intensity rainfalls are more likely to have differences in infiltration excess predictions with different rainfall resolutions and that a general understanding of the prevailing rainfall conditions and the soil's infiltration capacity can help in deciding whether high rainfall resolutions (below 1 h) are required for accurate surface runoff predictions.

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Vehicle Spatial Distribution and 3D Trajectory Extraction Algorithm in a Cross-Camera Traffic Scene

Sensors ◽

10.3390/s20226517 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6517

Author(s):

Xinyao Tang ◽

Huansheng Song ◽

Wei Wang ◽

Yanni Yang

Keyword(s):

Spatial Distribution ◽

Spatial Information ◽

Three Dimensional ◽

Physical Space ◽

Extraction Methods ◽

Geometric Constraints ◽

Panoramic Image ◽

Trajectory Data ◽

Comparison Algorithms ◽

Bounding Boxes

The three-dimensional trajectory data of vehicles have important practical meaning for traffic behavior analysis. To solve the problems of narrow visual angle in single-camera scenes and lack of continuous trajectories in 3D space by current cross-camera trajectory extraction methods, we propose an algorithm of vehicle spatial distribution and 3D trajectory extraction in this paper. First, a panoramic image of a road with spatial information is generated based on camera calibration, which is used to convert cross-camera perspectives into 3D physical space. Then, we choose YOLOv4 to obtain 2D bounding boxes of vehicles in cross-camera scenes. Based on the above information, 3D bounding boxes around vehicles are built with geometric constraints which are used to obtain projection centroids of vehicles. Finally, by calculating the spatial distribution of projection centroids in the panoramic image, 3D trajectories of vehicles are extracted. The experimental results indicate that our algorithm can effectively complete vehicle spatial distribution and 3D trajectory extraction in various traffic scenes, which outperforms other comparison algorithms.

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Skeleton Network Extraction and Analysis on Bicycle Sharing Networks

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2020070108 ◽

2020 ◽

Vol 16 (3) ◽

pp. 146-167

Author(s):

Kanokwan Malang ◽

Shuliang Wang ◽

Yuanyuan Lv ◽

Aniwat Phaphuangwittayakul

Keyword(s):

Spatial Distribution ◽

Extraction Method ◽

Spatial Information ◽

Spatial Clustering ◽

Transportation Networks ◽

Network Size ◽

Network Characteristics ◽

Spatial Features

Skeleton network extraction has been adopted unevenly in transportation networks whose nodes are always represented as spatial units. In this article, the TPks skeleton network extraction method is proposed and applied to bicycle sharing networks. The method aims to reduce the network size while preserving key topologies and spatial features. The authors quantified the importance of nodes by an improved topology potential algorithm. The spatial clustering allows to detect high traffic concentrations and allocate the nodes of each cluster according to their spatial distribution. Then, the skeleton network is constructed by aggregating the most important indicated skeleton nodes. The authors examine the skeleton network characteristics and different spatial information using the original networks as a benchmark. The results show that the skeleton networks can preserve the topological and spatial information similar to the original networks while reducing their size and complexity.

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