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Looking for (free) WMS of surface waters of Europe

Looking for (free) WMS of surface waters of Europe


Do you know any WMS with hydrographic map of Europe? Especially I am looking for map with surface waters of Europe


I'm sure one exists, however I have not found one yet. Alternatively, you could create your own WMS using data from the Natural Earth site.


Maybe this site is useful for you?

http://ccm.jrc.ec.europa.eu/php/index.php?action=view&id=24


Tracking barriers and their impacts on European river ecosystems

Healthy rivers require a high degree of continuity to support the complex life cycles of many aquatic species and a functioning ecosystem. However, for several decades, human interventions have disrupted river continuity and degraded river ecosystem functioning. Evidence from research shows that the continuity of most rivers in Europe is affected and that few free-flowing rivers remain in Europe.

Barriers alter a river’s natural flow. They can block the migration routes of fish and aquatic species both up- and downstream, with habitats becoming isolated through fragmentation. Disrupted continuity affects the reproduction patterns of migratory fish, such as salmon, eel and sturgeon. The transport of sediments in rivers is also blocked by barriers. This leads to accumulation of sediments upstream and a lack of sand and gravel downstream. As a result of all these factors, ecosystems and their processes can be severely affected and the habitat of aquatic flora and fauna can be dramatically altered.

In addition, the build-up of organic material and nutrients in reservoirs and in backwater from smaller dams often leads to a decrease in water quality, changes in temperature and the capacity to dissolve oxygen, and eutrophication (Gough et al., 2018).

The impacts of barriers vary according to their height and location. Barriers on rivers can differ significantly in size: small barriers may be only 10-50 cm high, while large dams can be higher than 15 m (Box 1). A major impact on a river may arise from a single, very damaging large structure or from the cumulative effects throughout the course of the river of a series of small structures, each of which may have only a small impact individually (EEA, 2018). The cumulative impact of a large number of river barriers in Europe is one of the leading causes of the more than 80% decline in freshwater biodiversity and the loss of 55% of monitored migratory fish populations (Birnie-Gauvin et al., 2018 Gough et al., 2018 Moberg and Singler, 2020).


FEATURED COMPANIES

  • Autodesk Inc.
  • General Electric Company
  • Hexagon AB
  • Maxar Technologies Inc.
  • MORE

2 Scope and Methodology
2.1 Objectives of the Study
2.2 Stakeholders
2.3 Data Sources
2.3.1 Primary Sources
2.3.2 Secondary Sources
2.4 Market Estimation
2.4.1 Bottom-Up Approach
2.4.2 Top-Down Approach
2.5 Forecasting Methodology

4 Introduction
4.1 Overview
4.2 Key Industry Trends

5 Global Geographic Information System (GIS) Market
5.1 Market Overview
5.2 Market Performance
5.3 Impact of COVID-19
5.4 Market Forecast

6 Market Breakup by Component
6.1 Hardware
6.1.1 Market Trends
6.1.2 Market Forecast
6.2 Software
6.2.1 Market Trends
6.2.2 Market Forecast
6.3 Services
6.3.1 Market Trends
6.3.2 Market Forecast

7 Market Breakup by Function
7.1 Mapping
7.1.1 Market Trends
7.1.2 Market Forecast
7.2 Surveying
7.2.1 Market Trends
7.2.2 Market Forecast
7.3 Telematics and Navigation
7.3.1 Market Trends
7.3.2 Market Forecast
7.4 Location-Based Services
7.4.1 Market Trends
7.4.2 Market Forecast

8 Market Breakup by Device
8.1 Desktop
8.1.1 Market Trends
8.1.2 Market Forecast
8.2 Mobile
8.2.1 Market Trends
8.2.2 Market Forecast

9 Market Breakup by End Use Industry
9.1 Agriculture
9.1.1 Market Trends
9.1.2 Market Forecast
9.2 Utilities
9.2.1 Market Trends
9.2.2 Market Forecast
9.3 Mining
9.3.1 Market Trends
9.3.2 Market Forecast
9.4 Construction
9.4.1 Market Trends
9.4.2 Market Forecast
9.5 Transportation
9.5.1 Market Trends
9.5.2 Market Forecast
9.6 Oil and Gas
9.6.1 Market Trends
9.6.2 Market Forecast
9.7 Others
9.7.1 Market Trends
9.7.2 Market Forecast

10 Market Breakup by Region
10.1 North America
10.1.1 United States
10.1.1.1 Market Trends
10.1.1.2 Market Forecast
10.1.2 Canada
10.1.2.1 Market Trends
10.1.2.2 Market Forecast
10.2 Asia Pacific
10.2.1 China
10.2.1.1 Market Trends
10.2.1.2 Market Forecast
10.2.2 Japan
10.2.2.1 Market Trends
10.2.2.2 Market Forecast
10.2.3 India
10.2.3.1 Market Trends
10.2.3.2 Market Forecast
10.2.4 South Korea
10.2.4.1 Market Trends
10.2.4.2 Market Forecast
10.2.5 Australia
10.2.5.1 Market Trends
10.2.5.2 Market Forecast
10.2.6 Indonesia
10.2.6.1 Market Trends
10.2.6.2 Market Forecast
10.2.7 Others
10.2.7.1 Market Trends
10.2.7.2 Market Forecast
10.3 Europe
10.3.1 Germany
10.3.1.1 Market Trends
10.3.1.2 Market Forecast
10.3.2 France
10.3.2.1 Market Trends
10.3.2.2 Market Forecast
10.3.3 United Kingdom
10.3.3.1 Market Trends
10.3.3.2 Market Forecast
10.3.4 Italy
10.3.4.1 Market Trends
10.3.4.2 Market Forecast
10.3.5 Spain
10.3.5.1 Market Trends
10.3.5.2 Market Forecast
10.3.6 Russia
10.3.6.1 Market Trends
10.3.6.2 Market Forecast
10.3.7 Others
10.3.7.1 Market Trends
10.3.7.2 Market Forecast
10.4 Latin America
10.4.1 Brazil
10.4.1.1 Market Trends
10.4.1.2 Market Forecast
10.4.2 Mexico
10.4.2.1 Market Trends
10.4.2.2 Market Forecast
10.4.3 Others
10.4.3.1 Market Trends
10.4.3.2 Market Forecast
10.5 Middle East and Africa
10.5.1 Market Trends
10.5.2 Market Breakup by Country
10.5.3 Market Forecast

11 SWOT Analysis
11.1 Overview
11.2 Strengths
11.3 Weaknesses
11.4 Opportunities
11.5 Threats

12 Value Chain Analysis

13 Porters Five Forces Analysis
13.1 Overview
13.2 Bargaining Power of Buyers
13.3 Bargaining Power of Suppliers
13.4 Degree of Competition
13.5 Threat of New Entrants
13.6 Threat of Substitutes

15 Competitive Landscape
15.1 Market Structure
15.2 Key Players
15.3 Profiles of Key Players
15.3.1 Autodesk Inc.
15.3.1.1 Company Overview
15.3.1.2 Product Portfolio
15.3.1.3 Financials
15.3.1.4 SWOT Analysis
15.3.2 Bentley Systems Incorporated
15.3.2.1 Company Overview
15.3.2.2 Product Portfolio
15.3.3 Caliper Corporation (PSI Services LLC)
15.3.3.1 Company Overview
15.3.3.2 Product Portfolio
15.3.4 Computer Aided Development Corporation Limited (Cadcorp)
15.3.4.1 Company Overview
15.3.4.2 Product Portfolio
15.3.4.3 Financials
15.3.5 Environmental Systems Research Institute Inc.
15.3.5.1 Company Overview
15.3.5.2 Product Portfolio
15.3.5.3 SWOT Analysis
15.3.6 General Electric Company
15.3.6.1 Company Overview
15.3.6.2 Product Portfolio
15.3.6.3 Financials
15.3.6.4 SWOT Analysis
15.3.7 Geosoft Inc. (Seequent Ltd.)
15.3.7.1 Company Overview
15.3.7.2 Product Portfolio
15.3.8 Hexagon AB
15.3.8.1 Company Overview
15.3.8.2 Product Portfolio
15.3.8.3 Financials
15.3.8.4 SWOT Analysis
15.3.9 Maxar Technologies Inc.
15.3.9.1 Company Overview
15.3.9.2 Product Portfolio
15.3.9.3 Financials
15.3.10 SuperMap Software Co. Ltd.
15.3.10.1 Company Overview
15.3.10.2 Product Portfolio
15.3.10.3 Financials
15.3.11 Topcon Corporation
15.3.11.1 Company Overview
15.3.11.2 Product Portfolio
15.3.11.3 Financials
15.3.12 Trimble Inc.
15.3.12.1 Company Overview
15.3.12.2 Product Portfolio
15.3.12.3 Financials
15.3.12.4 SWOT Analysis

List of Figures
Figure 1: Global: Geographic Information System Market: Major Drivers and Challenges
Figure 2: Global: Geographic Information System Market: Sales Value (in Billion US$), 2015-2020
Figure 3: Global: Geographic Information System Market: Breakup by Component (in %), 2020
Figure 4: Global: Geographic Information System Market: Breakup by Function (in %), 2020
Figure 5: Global: Geographic Information System Market: Breakup by Device (in %), 2020
Figure 6: Global: Geographic Information System Market: Breakup by End Use Industry (in %), 2020
Figure 7: Global: Geographic Information System Market: Breakup by Region (in %), 2020
Figure 8: Global: Geographic Information System Market Forecast: Sales Value (in Billion US$), 2021-2026
Figure 9: Global: Geographic Information System (Hardware) Market: Sales Value (in Million US$), 2015 & 2020
Figure 10: Global: Geographic Information System (Hardware) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 11: Global: Geographic Information System (Software) Market: Sales Value (in Million US$), 2015 & 2020
Figure 12: Global: Geographic Information System (Software) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 13: Global: Geographic Information System (Services) Market: Sales Value (in Million US$), 2015 & 2020
Figure 14: Global: Geographic Information System (Services) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 15: Global: Geographic Information System (Mapping) Market: Sales Value (in Million US$), 2015 & 2020
Figure 16: Global: Geographic Information System (Mapping) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 17: Global: Geographic Information System (Surveying) Market: Sales Value (in Million US$), 2015 & 2020
Figure 18: Global: Geographic Information System (Surveying) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 19: Global: Geographic Information System (Telematics and Navigation) Market: Sales Value (in Million US$), 2015 & 2020
Figure 20: Global: Geographic Information System (Telematics and Navigation) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 21: Global: Geographic Information System (Location-Based Services) Market: Sales Value (in Million US$), 2015 & 2020
Figure 22: Global: Geographic Information System (Location-Based Services) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 23: Global: Geographic Information System (Desktop) Market: Sales Value (in Million US$), 2015 & 2020
Figure 24: Global: Geographic Information System (Desktop) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 25: Global: Geographic Information System (Mobile) Market: Sales Value (in Million US$), 2015 & 2020
Figure 26: Global: Geographic Information System (Mobile) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 27: Global: Geographic Information System (Agriculture) Market: Sales Value (in Million US$), 2015 & 2020
Figure 28: Global: Geographic Information System (Agriculture) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 29: Global: Geographic Information System (Utilities) Market: Sales Value (in Million US$), 2015 & 2020
Figure 30: Global: Geographic Information System (Utilities) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 31: Global: Geographic Information System (Mining) Market: Sales Value (in Million US$), 2015 & 2020
Figure 32: Global: Geographic Information System (Mining) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 33: Global: Geographic Information System (Construction) Market: Sales Value (in Million US$), 2015 & 2020
Figure 34: Global: Geographic Information System (Construction) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 35: Global: Geographic Information System (Transportation) Market: Sales Value (in Million US$), 2015 & 2020
Figure 36: Global: Geographic Information System (Transportation) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 37: Global: Geographic Information System (Oil and Gas) Market: Sales Value (in Million US$), 2015 & 2020
Figure 38: Global: Geographic Information System (Oil and Gas) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 39: Global: Geographic Information System (Other End Use Industries) Market: Sales Value (in Million US$), 2015 & 2020
Figure 40: Global: Geographic Information System (Other End Use Industries) Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 41: North America: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 42: North America: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 43: United States: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 44: United States: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 45: Canada: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 46: Canada: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 47: Asia Pacific: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 48: Asia Pacific: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 49: China: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 50: China: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 51: Japan: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 52: Japan: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 53: India: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 54: India: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 55: South Korea: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 56: South Korea: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 57: Australia: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 58: Australia: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 59: Indonesia: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 60: Indonesia: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 61: Others: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 62: Others: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 63: Europe: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 64: Europe: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 65: Germany: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 66: Germany: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 67: France: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 68: France: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 69: United Kingdom: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 70: United Kingdom: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 71: Italy: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 72: Italy: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 73: Spain: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 74: Spain: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 75: Russia: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 76: Russia: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 77: Others: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 78: Others: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 79: Latin America: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 80: Latin America: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 81: Brazil: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 82: Brazil: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 83: Mexico: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 84: Mexico: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 85: Others: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 86: Others: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 87: Middle East and Africa: Geographic Information System Market: Sales Value (in Million US$), 2015 & 2020
Figure 88: Middle East and Africa: Geographic Information System Market Forecast: Sales Value (in Million US$), 2021-2026
Figure 89: Global: Geographic Information System Industry: SWOT Analysis
Figure 90: Global: Geographic Information System Industry: Value Chain Analysis
Figure 91: Global: Geographic Information System Industry: Porter’s Five Forces Analysis

List of Tables
Table 1: Global: Geographic Information System Market: Key Industry Highlights, 2020 and 2026
Table 2: Global: Geographic Information System Market Forecast: Breakup by Component (in Million US$), 2021-2026
Table 3: Global: Geographic Information System Market Forecast: Breakup by Function (in Million US$), 2021-2026
Table 4: Global: Geographic Information System Market Forecast: Breakup by Device (in Million US$), 2021-2026
Table 5: Global: Geographic Information System Market Forecast: Breakup by End Use Industry (in Million US$), 2021-2026
Table 6: Global: Geographic Information System Market Forecast: Breakup by Region (in Million US$), 2021-2026
Table 7: Global: Geographic Information System Market Structure
Table 8: Global: Geographic Information System Market: Key Players


Masters Programs in Geographic Sciences in Europe 2021

Geographic sciences can include an education on the computational methods and data structures used to represent, analyze and process geographic information. In addition, geographic science students may have the opportunity to study Earth&rsquos physical features.In all, there are over 4000 Higher Education Institutions in Europe offering a wide range of courses at Bachelor, Masters and Doctorate&hellip Read more

Geographic sciences can include an education on the computational methods and data structures used to represent, analyze and process geographic information. In addition, geographic science students may have the opportunity to study Earth&rsquos physical features.

In all, there are over 4000 Higher Education Institutions in Europe offering a wide range of courses at Bachelor, Masters and Doctorate level. With more and more of these organizations offering English as the language of education for at least some of their degree programs, universities in Europe are now of higher quality than ever before. Universities in Europe offer a friendly welcome to foreign students and to give a course of knowledge that meets their profession needs in today’s global demand.


Weather Systems

A high pressure system, also known as an anticyclone occurs when the weather is dominated by stable conditions. Under an anticyclone air is descending, forming an area of higher pressure at the surface. Because of these stable conditions, cloud formation is inhibited, so the weather is usually settled with only small amounts of cloud cover. In the Northern Hemisphere winds blow in a clockwise direction around an anticyclone. As isobars are normally widely spaced around an anticyclone, winds are often quite light.

Anticyclones can be identified on weather charts as an often large area of widely spaced isobars, where pressure is higher than surrounding areas.

Winter anticyclones

In winter the clear, settled conditions and light winds associated with anticyclones can lead to frost and fog. The clear skies allow heat to be lost from the surface of the earth by radiation, allowing temperatures to fall steadily overnight, leading to air or ground frosts. Light winds along with falling temperatures can encourage fog to form this can linger well into the following morning and be slow to clear. If high pressure becomes established over Northern Europe during winter this can bring a spell of cold easterly winds to the UK.

Summer anticyclones

In summer the clear settled conditions associated with anticyclones can bring long sunny days and warm temperatures. The weather is normally dry, although occasionally, very hot temperatures can trigger thunderstorms. An anticyclone situated over the UK or near continent usually brings warm, fine weather.

Low pressure systems

A low pressure system, also known as a depression occurs when the weather is dominated by unstable conditions. Under a depression air is rising, forming an area of low pressure at the surface. This rising air cools and condenses and helps encourage cloud formation, so the weather is often cloudy and wet. In the Northern Hemisphere winds blow in anticlockwise direction around a depression. Isobars are normally closely spaced around a depressions leading to strong winds.

Depressions can be identified on weather charts as an area of closely spaced isobars, often in a roughly circular shape, where pressure is lower than surrounding areas. They are often accompanied by fronts.

What to do next

Using this information on pressure systems you should now be able to complete worksheet 1.

Then you can complete Extension 1 or worksheet 2.

Part B

Anticyclones, Depressions and Fronts

Part B – Fronts

A front is a boundary between two different types of air masses, these are normally warm moist air masses from the tropics and cooler drier air masses from polar regions. Fronts move with the wind so over the UK they normally move from west to east. The notes below provide information about the most common types of fronts. The descriptions given apply to active well developed fronts, weaker fronts may not display all the characteristics or they may be less well defined.

Warm fronts

A warm front indicates that warm air is advancing and rising up over the colder air. This is because the warm air is ‘lighter’ or less dense, than the cold air. Therefore warm fronts occur where warmer air is replacing cooler air at the surface. As the warm front approaches there is a gradual deterioration in the weather. Clouds gradually lower from higher cirrus, through altostratus, to stratus and nimbostratus at the front. There is often a prolonged spell of rainfall which is often heavy. Behind the warm front the rain becomes lighter, turns to drizzle or ceases, but it remains cloudy. Temperatures rise behind the warm front and winds turn clockwise, also known as a wind ‘veer’. Pressure falls steadily ahead of and during the passage of the warm front, but then rises slowly after its passage.

The diagram below shows the formation of a warm front in diagrammatic form.

The diagram below shows a cross section through a warm front, with associated cloud, temperature and weather changes.

Cold fronts

A cold front indicates that cold air is advancing and pushing underneath warmer air at the surface. This occurs because the cold air is ‘heavier’ or denser than the warm air. Therefore cold fronts occur where cooler air is replacing warmer air at the surface. The passage of weather associated with a cold front is much shorter lived than that with a warm front. As there is often a lot of cloud in the warmer air ahead of the cold front, there is often little indication of the approaching cold front. As the front passes temperatures fall and there is often a short spell of very heavy rain, sometimes with inbedded thunderstorms and cumulonimbus clouds. Behind the front the weather is much brighter with broken clouds but occasional showers. Winds veer with the passage of the cold front and are often strong and gusty, especially near showers. Pressure rises throughout the approach and passage of the cold front.

The diagram below shows the formation of a cold front in diagrammatic form.

The diagram below shows a cross section through a cold front, with associated cloud, temperature and weather changes.

Occlusions

In a mature depression the warm front normally precedes the cold front. Cold fronts generally travel much quicker than warm fronts, and eventually it will catch up with the warm front. Where the two fronts meet, warm air is lifted from the surface and an occlusion is formed. An occlusion can be thought of as having similar characteristics to both warm and cold fronts. The weather ahead of an occlusion is similar to that ahead of a warm front, whilst the weather behind is similar to that behind a cold front.

The diagrams below depict the formation of an occlusion.

The diagram below shows the occlusion in cross section.

What to do next

Anticyclones, Depressions and Fronts

Part C – Life cycle of a Depression

Origin and infancy

Occlusion

Death

Anticyclones, Depressions and Fronts

Part D – Depression cross-section and weather sequence

Cross-section through a classic Depression

Most depressions have a warm and cold front, more mature depressions may also have an occluded front. The diagram below shows a cross-section through a depression, showing the warm and cold fronts and an indication of the associated weather.

What to do next

Using this information on the passage of depressions you should now be able to complete worksheet 3, and worksheet 4.


Methods

Model approach

Our global model computes plastic load inputs from 40,760 watersheds worldwide 21 into the ocean using geospatial data on population density 18,19 and MPW production per inhabitant and per day in 182 individual countries 5,20 . Waste is considered mismanaged when it is littered or inadequately disposed. MPW corresponds to the fraction of plastic found in mismanaged waste material on land. This definition was applied in a previous estimate of global plastic waste inputs into the ocean from coastal population worldwide 5 . MPW production rates are integrated inside catchments. The resulting mass is accumulated following natural drainage patterns derived from local topography, and taking into account the presence of artificial barriers (for example, dams and weirs) acting as sinks. The seasonality of inputs at the outfall location is derived from monthly average catchment runoff. An empirical relation using integrated MPW mass production upstream of river mouths and seasonal runoff is formulated and calibrated using a set of field observations (Fig. 4).

Plastic mass production per river catchment (Mmpw n=40,760 rivers) is computed from data on MPW production rates per country, population density, topographic elevation and location of artificial barriers. Seasonality of inputs is derived from monthly averaged runoff (R). A parametric equation with parameters k and a is used to fit model predictions (Mout) against results from observational studies. For our mid-point estimate, best fit was found for k=1.85 10 −3 and a=1.52 (r 2 =0.93, n=30).

Model formulation

To estimate daily plastic mass input from individual watersheds, we used the following parametric equation:

where Mout is the plastic mass release at the outflow in kilogram per day, Mmpw, the mass of MPW produced inside the catchment downstream of artificial barriers and R, monthly averaged catchment runoff. k and a are the regression parameters. We find a strong coefficient of determination (r 2 =0.93) for k=1.85 10 –3 and a=1.52 (midpoint estimate, Fig. 2) using n=30 records from 13 different rivers, where data on plastic contamination in surface waters were reported in the literature.

We considered only peer-reviewed studies that provided reliable estimates of plastic concentrations (number and/or mass of plastic particles per volume and/or area of river water) using surface net devices. For plastic concentrations reported in number of particles per unit area of river surface, we used the depth of the trawling devices to convert reported surface areas (km 2 ) into volume of water sampled (m 3 ). The bibliographic review and selection criteria described above led to the consideration of seven studies in our model calibration exercise. These studies reported river plastic concentrations in 13 rivers, at 30 sampling events that occurred in different time periods (Table 2). Our approach is conservative because we neglected the contribution of buoyant plastic that may occur below the sampled depth due to water turbulence 13 . Furthermore, this approach does not account for the contribution of non-buoyant plastics that, once introduced in rivers, may slowly make its way to the oceans due to turbulent transport, accumulating in deep sea river canyons 38 . Around 48% of the plastic produced yearly is made of polymers lighter than seawater (Polyethylene and Polypropylene 1 ), this number is likely higher due to existence of objects made of polymers heavier than seawater that can float due to air entrapment (for example, PET bottles and foamed Polystyrene).

Not all studies considered here reported micro- and macro-plastic concentrations at surface waters of rivers. As such, for our midpoint estimate, we homogenized our data set using the mean ratio micro- to macro-plastic numerical concentration from studies reporting both types (mean ratio equals to 0.04). For comparison, a study compiling thousands of samples at sea found a relatively similar mean ratio of 0.07 (ref. 29). When only numerical concentrations were reported, we estimated the mass concentrations using the average mass of micro- and macro-plastic particles sampled at sea: 0.003 and 0.17 g, respectively 29 . The results of the standardization exercise are presented in Table 3.

We acknowledge however, that the extrapolations described above are a limitation of the calibration exercise presented here, as the average mass of river plastic particles, as well as the ratio between micro- and macro-plastic concentrations may vary across catchments due to local differences in in-situ fragmentation rates, plastic transport processes and levels of primary micro-plastic emissions (for example, pre-production pellets, microbeads from cosmetics and hygienic products, laundry powders, paint and coating flakes). A sensitivity analysis was conducted by varying the mean ratio micro- to macro-plastic numerical concentration (range: 0.01–0.12) and the average mass of particles (range: 0.002–0.004 g and 0.04–0.33 g for micro- and macro-plastic, respectively) using range values found at sea 29 . We determined an upper and lower input estimate using equation (1) with respectively k=1.07 10 –3 , a=1.61 (r 2 =0.93, n=30) and k=4.46 10 −3 , a=1.42 (r 2 =0.91, n=30). Further details on the sensitivity analysis are provided in Supplementary Table 1.

Correction for surface waters

Some studies 8,9 directly provide an estimate of daily or yearly plastic mass input rate. For the other studies, we computed the daily releases of plastic from rivers into the ocean by multiplying the estimated mass concentrations from observations by the volume of water flowing at the surface layer per day. For each river, the surface layer thickness was taken at the sampling depth reported by the study, therefore the contribution of any particles suspended below the sampled depth was neglected. We derived the surface layer flow from the river depth and the total monthly averaged discharge predicted by our hydrological model using the month corresponding to the surveyed time period. When the river depth was not reported by the study, we used the following relationship in equation (2) between channel form and discharge 39 :

where Q is the river discharge, D is the river depth, c and f are parameters. A good coefficient of determination (r 2 =0.75) was found for c=0.349 and f=0.341, when comparing discharge and bed form of 674 rivers in Canada and USA 40 . When studies reported surveys directly from estuaries, the depth was estimated using nautical charts.

Estimating MPW mass in catchments worldwide

We combined data on waste generation in kilograms per inhabitant and per day for 182 individual countries 5,20 with gridded population densities in inhabitants per km 2 (refs 18, 19) to estimate inland MPW production rates per year. An exception was made for Sri Lanka, where we replaced the World Bank statistics with values reported in more detailed regional assessments 41,42 . We computed a global ¼ degree resolution grid of estimated mass of MPW generation on land in tonnes per year. In this model, we assumed that inland plastic is accumulated by following natural drainage patterns, derived from the space borne elevation data 22 . The global landmass surface area was divided in river catchments from the U.S. Geological Survey Agency that are used by the Global Land Data Assimilation System (GLDAS, ref. 21). We used the flow accumulation toolset from ESRI’s ArcGIS software to compute the total mass of inland MPW upstream of the outflow location. The outflow is the most downstream position in a river catchment and determines the input source point into the ocean. Input from catchments with an outflow not connected to the ocean (for example, specifically arid inland areas) were discarded. The model takes into account the presence of artificial barriers and treats them as accumulation sinks, where plastic at the surface is intercepted. As a result, the predicted plastic concentration at the river mouth is representative of the accumulation of inland MPW (in tonnes per year) in the catchment area downstream of artificial dams.

The consideration of dams in our numerical model was motivated by a better correlation found with measurements (Table 4) than when including MPW production rates upstream of dams. Artificial barriers in rivers may retain 65% of the global input into freshwater, as we calculated an annual 2.13–4.46 million tonnes of plastic introduced upstream of dams that are not accounted as input into the oceans by our model. These results were calculated using the parametric equation determined when considering MPW downstream of dams as model proxy. Including MPW production upstream of dams, when assessing the linear regression would result in different model parameters k and a in equation (1). While determining regression coefficients based on MPW quantities upstream of dams, our model predicted a global input of 0.76–1.55 million tonnes per year (midpoint at 0.91 million tonnes per year) which remains in the same order of magnitude than the initial scenario. The decrease in predicted global input from the current model may be explained by the number of dams present in the large rivers covered by the observational studies. In the Yangtze River and Danube River catchments particularly, respectively 68% and 78% of MPW production occurs upstream of a dam. Therefore, relative MPW mass have less weight on the overall prediction result which ultimately leads in a decrease of our global estimate.

Dam locations were derived from the United Nation Food and Agriculture Organization’s AquaStat dam database 23 , consisting of 8,800 dams worldwide with a minimum height of 15 m or a reservoir capacity of >3 million m 3 . The Global Rivers and Dam Database (GRanD database ref. 24) was used for South America as the AquaStat database was incomplete for this continent. The catchments containing the largest number of dams were the basins of the Mississippi River (718 dams), Yangtze River (342 dams) and Danube River (184 dams). As the analysis is based on natural drainage patterns, the model limitations are that man-made channels are not taken into account and that plastic load accumulates in the main arms of rivers at deltas, introducing uncertainties at local scales. These limitations however do not affect the global inputs estimate. An example for the island of Java in Indonesia illustrating the different datasets involved in this framework is provided in Fig. 5.

(a) Estimated MPW production rates in t yr −1 . (b) Accumulated MPW production in rivers and location of artificial barriers. (c) Predicted plastic mass input into the ocean at river mouths in t d −1 .

Estimating monthly averaged catchment runoff

In our model, surface runoff is included as a model parameter to account for (1) the introduction of MPW in riverine system during episodes of heavy rainfall 10 and (2) the remobilization of deposited plastic particles during flood events 31 . Monthly averaged catchment runoff in millimetres per day was calculated using GLDAS driving the NOAH Land Surface Model 21 . This land surface modelling system integrates data from advanced ground and space-based observation systems. The model contains land surface parameters for vegetation, soil, elevation and slope. The forcing data in the model are near-real-time satellite-derived precipitation and evaporation data (wind, radiation, temperature, humidity and surface pressure). The model computes the daily surface and subsurface runoff globally on ¼ degree resolution, by solving terrestrial water and energy budgets 21 . Subsurface runoff consists of water that infiltrates into the soil and flows to a water body by groundwater flow. Surface runoff occurs either when the rainfall exceeds the infiltration capacity of the soil or when the soil is saturated with groundwater. Monthly and yearly averages are calculated over the period 2005–2015. The surface and subsurface runoff are summed and subsequently averaged per catchment area 22 . A better correlation was found with estimated flux inputs from observational studies when considering monthly averaged runoff instead of the yearly average (Table 4). Therefore, monthly averaged catchment runoff corresponding to sampling event month was considered while calibrating our model to account for temporal variations and seasonality of inputs.

The main motivation behind using runoff data from GLDAS is the provision of land surface processes including runoff estimates at a global level. Nonetheless, it is important to notice that comparisons between river discharge predictions from GLDAS and observations in 66 basins worldwide 43 demonstrated that predictions are somewhat dryer than observations. The authors of this validation study attributed the differences to uncertainties in precipitation rates. As our framework relies on intra-annual variability, the NOAH land surface model predictions, forced with GLDAS, were still in good agreement with seasonal variations measurements with a predicted date of maximum discharge within 20 days of observed annual discharge peak date for most rivers covered in the GLDAS validation study.

Data availability

The authors declare that the main data supporting the findings of this study are available within the article and its Supplementary Information. Global model inputs and outputs for lower, midpoint and upper estimates and for the 40,760 catchments considered in this study have been deposited in geospatial vector data format for geographic information system (GIS) software on figshare with the identifier doi:10.6084/m9.figshare.4725541.


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Executive Solution Architect, UDC

GIS System Pricing

The cost of your GIS implementation is dependent upon several key factors including how your organization will use the system, your need for advanced capabilities and the state and quality of your existing data.

Getting Started – Base GIS System

The base install of GIS editing and web browsing software will be in the $500K to $800K range plus the GIS product vendor’s cost of their ELA being driven by number of users. Depending on which GIS vendor, you may want to add to the base GIS technology software products that were built specifically for electric utilities to manage the daily maintenance of their electric network which will add $250K to $400K. These products have software licensing tied to number of editors.

Maintaining your as-designed and as-built states of your electric network is the primary function of the data editing within a GIS.

Enterprise GIS for Advanced Capabilities

The high value of an enterprise GIS is using it to feed the network to your operational systems: ADMS or OMS engineering planning systems and work management systems. With modern enterprise GIS technology that includes mobile GIS applications, we believe using the GIS to plan, create, dispatch, perform, monitor and report on for all planned compliance, inspection and maintenance work is a great use of enterprise GIS technology with the integration to your EAM of choice with all job completion details including labor and materials. Using the spatial BI power of the enterprise GIS makes it a natural technology for things like reliability engineering analysis and outage communication dashboards.

UDC has an electric enterprise GIS reference architecture that addresses using the enterprise GIS in all of these electric energy delivery business areas and integrating the GIS with the other business domain systems: ADMS, OMS, DERMS, CIS, CRM, AMI, MDMS, MWM, EAM, Engineering, GWD, CMS and CAD. These are typically broken up into business releases that are focused on a few business applications and the related set of enterprise GIS applications and integrations. Based on the number of related energy delivery systems in use, a range of $6M to $10M depending if you take advantage of some of the enterprise GIS implementation frameworks available that bring enterprise GIS applications and field applications. UDC does perform both enterprise GIS and ADMS roadmaps for our electric utility clients and can help with business cases to secure funding for an enterprise GIS and ADMS implementations. As GIS has grown into the enterprise software space, there are some change management challenges as well that will need to be addressed with an enterprise GIS deployment.

Preparing Your Data for GIS

Last but not least, a GIS needs to be populated with your circuits and substations. If going from paper and CAD to GIS, we call this data conversion and its cost is based on number of data sources that must be touched in order to populate the GIS manually. If you are upgrading from an existing GIS, then it should be a digital data migration. Data Conversions can run into the tens of millions depending on number of circuits and customers. Data Migrations will be in the $500K - $800K range if you are taking advantage of data migration frameworks and depending on the quality of the data in the current GIS and how much of the newer functionality found in modern GIS’s is looking to be enabled.


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