Extent, regional distribution and changes in area of different classes of wetland

2018 ◽  
Vol 69 (10) ◽  
pp. 1525 ◽  
Author(s):  
Nick C. Davidson ◽  
C. Max Finlayson
Keyword(s):  
Coastal Wetlands ◽  
Regional Distribution ◽  
Rice Paddy ◽  
Wetland Area ◽  
Inland Wetlands ◽  
Data Gaps ◽  
Natural Wetlands ◽  
Wetland Extent ◽  
Almost All ◽  
Surface Wetland

We compiled available data and information on the global and regional areas (Ramsar regions), and changes in area, of 22 classes of marine or coastal and inland wetlands. From those classes for which there is information, inland natural surface wetlands (forming ~77% of total surface wetland extent) are dominated by non-forested peatlands, marshes and swamps on alluvial soils, with peatlands forming ~33% of natural inland wetlands. The smaller area of marine or coastal wetlands (~10% of total wetland extent) is dominated by unvegetated tidal flats and saltmarshes. Largest areas of human-made wetlands for which there is information are rice paddy and water storage bodies, with a much smaller area of tropical oil palm and pulpwood plantations. These human-made wetlands are all increasing in area. The reported decline in global natural wetland area is occurring across almost all classes of inland and marine or coastal natural wetlands. Total global wetland area estimated from these wetland classes is between 15.2×106 and 16.2×106km2, similar to recent global wetland area estimates derived from remote sensing. Given the considerable data gaps for area of wetland classes, even the most recent other estimates of global wetland extent are likely to be underestimates.

scholarly journals Impacts of climate and reclamation on temporal variations in CH<sub>4</sub> emissions from different wetlands in China: from 1950 to 2010

2015 ◽  
Vol 12 (9) ◽  
pp. 7055-7091 ◽  
Author(s):  
T. Li ◽  
W. Zhang ◽  
Q. Zhang ◽  
Y. Lu ◽  
G. Wang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Climate Warming ◽  
Coastal Wetlands ◽  
Temporal Variations ◽  
Northeastern China ◽  
Large Temperature ◽  
Modeling Framework ◽  
Ch4 Emissions ◽  
Inland Wetlands ◽  
Natural Wetlands

Abstract. Natural wetlands are among the most important sources of methane; thus, these areas are important for better understanding long-term temporal variations in atmospheric methane concentration. During the last 60 years, wetlands have experienced extensive conversion and global impacts from climate warming, which makes the estimation of methane emission from wetlands highly uncertain. In this paper, we present a modeling framework, integrating CH4MODwetland, TOPMODEL and TEM models, to analyze the temporal and spatial variations in CH4 emissions from natural wetlands (including inland wetlands, coastal wetlands, lakes and rivers) in China. Our analysis revealed an increase of 25.5%, averaging 0.52 g m−2 per decade, in national CH4 fluxes from 1950 to 2010, which was mainly induced by climate warming. Higher rates of increasing CH4 fluxes occurred in northeastern, northern and northwestern China, associated with large temperature increases. However, decreases in precipitation due to climate warming offset the increase in CH4 fluxes in these regions. The CH4 fluxes from the wetland on the Qinghai Tibetan Plateau exhibited a lower rate of increase, which was approximately 25% of that simulated in northeastern China. Although climate warming has accelerated CH4 fluxes, the total amount of national CH4 emissions decreased by approximately 2.35 Tg (1.91–2.81 Tg), i.e., from 4.50 Tg in the early 1950s to 2.15 Tg in the late 2000s, due to a large wetland loss of 17.0 million ha. Of this reduction, 0.26 Tg (0.24–0.28 Tg) was derived from lakes and rivers, 0.16 Tg (0.13–0.20 Tg) from coastal wetlands, and 1.92 Tg (1.54–2.33 Tg) from inland wetlands. Northeastern China had the largest contribution to this reduction, with a loss of 1.68 Tg. The CH4 emissions were reduced by more than half in most regions in China except for the Qinghai Tibetan Plateau, where only a 23.3% decrease in CH4 was observed.

scholarly journals Analysis of Characteristics and Driving Factors of Wetland Landscape Pattern Change in Henan Province from 1980 to 2015

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 564
Author(s):  
Heying Li ◽  
Jiayao Wang ◽  
Jianchen Zhang ◽  
Fen Qin ◽  
Jiyuan Hu ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Constructed Wetlands ◽  
Redundancy Analysis ◽  
Yellow River ◽  
Land Development ◽  
Henan Province ◽  
Driving Factors ◽  
Wetland Area ◽  
Natural Wetlands ◽  
Temporal And Spatial

The study of the temporal and spatial evolution of wetland landscapes and its driving factors is an important reference for wetland ecological restoration and protection. This article utilized seven periods of land use data in Henan Province from 1980 to 2015 to extract the spatial distribution characteristics of wetlands and analyze the temporal and spatial changes of wetlands in Henan Province. Transfer matrix, landscape metrics, correlation analysis, and redundancy analysis were applied to calculate and analyze the transformation types and area of wetland resources between all consecutive periods, and then the main driving factors of wetland expansion/contraction were explored. First, the total wetland area in Henan Province increased by 28% from 1980 to 2015, and the increased wetland area was mainly constructed wetlands, including paddy field, reservoir and pond, and canal. Natural wetlands such as marsh, lake, and floodplain decreased by 74%. Marsh area declined the most during 1990–1995, and was mainly transformed into floodplain and “Others” because of agricultural reclamation, low precipitation, and low Yellow River runoff. The floodplain area dropped the most from 2005 to 2010, mainly converted to canals and “Others” because of reclamation, exploitation of groundwater, the construction of the South–to–North Water Transfer Project, and recreational land development. Second, the results of correlation analysis and redundancy analysis indicated that economic factors were positively correlated with the area of some constructed wetlands and negatively correlated with the area of some natural wetlands. Socioeconomic development was the main driving factors for changes in wetland types. The proportion of wetland habitat in Henan Province in 2015 was only 0.3%, which is low compared to the Chinese average of 2.7%. The government should pay more attention to the restoration of natural wetlands in Henan Province.

scholarly journals Comparison of Qinzhou bay wetland landscape information extraction by three methods

2014 ◽  
Vol XL-4 ◽  
pp. 21-28 ◽  
Author(s):  
X. Chang ◽  
Q. Zhang ◽  
M. Luo ◽  
C. Dong
Keyword(s):  
Coastal Wetlands ◽  
Supervised Classification ◽  
Coastal Wetland ◽  
Object Oriented ◽  
Visual Interpretation ◽  
Wetland Area ◽  
The Third ◽  
Tasseled Cap Transformation ◽  
Qinzhou Bay ◽  
Landscape Information

Wetland ecosystem plays an important role on the environment and sustainable socio-economic development. Based on the TM images in 2010 with a pretreament of Tasseled Cap transformation, three different methods are used to extract the Qinzhou Bay coastal wetlands using Supervised Classification (SC), Decision Trees (DT) and Object -oriented (OO) methods. Firstly coastal wetlands are picked out by artificial visual interpretation as discriminant standard. The result shows that when the same evaluation template used, the accuracy and Kappa coefficient of SC, DT and OO are 92.00 %, 0.8952; 89.00 %, 0.8582; 91.00 %, 0.8848 respectively. The total area of coastal wetland is 218.3 km<sup>2</sup> by artificial visual interpretation, and the extracted wetland area of SC, DT and OO is 219 km<sup>2</sup>, 193.70 km<sup>2</sup>, 217.40 km<sup>2</sup> respectively. The result indicates that SC is in the f irst place, followed by OO approach, and the third DT method when used to extract Qingzhou Bay coastal wetland.

scholarly journals Wetlands of International Importance: Status, Threats, and Future Protection

2019 ◽  
Vol 16 (10) ◽  
pp. 1818 ◽  
Author(s):  
Ting Xu ◽  
Baisha Weng ◽  
Denghua Yan ◽  
Kun Wang ◽  
Xiangnan Li ◽  
...  
Keyword(s):  
Coastal Wetlands ◽  
Wetland Management ◽  
Impact Factors ◽  
Natural Wetland ◽  
Land Area ◽  
Large Space ◽  
International Importance ◽  
Natural Wetlands ◽  
Impact On Biodiversity ◽  
Lake Wetlands

The 2303 Wetlands of International Importance distribute unevenly in different continents. Europe owns the largest number of sites, while Africa has the largest area of sites. More than half of the sites are affected by three or four impact factors (55%). The most significant impact factors are pollution (54%), biological resources use (53%), natural system modification (53%), and agriculture and aquaculture (42%). The main affected objects are land area and environment of the wetlands, occurred in 75% and 69% of the sites, respectively. The types most affected by land area occupation are river wetlands and lake wetlands, the types with the greatest impact on environment are marine/coastal wetlands and river wetlands, the type with the greatest impact on biodiversity is river wetlands, the types most affected by water resources regulation are marsh wetlands and river wetlands, and the types most affected by climate change are lake wetlands and marine/coastal wetlands. About one-third of the wetland sites have been artificially reconstructed. However, it is found that the proportions of natural wetland sites not affected or affected by only one factor are generally higher than that of wetland sites both containing natural wetlands and human-made wetlands, while the proportions of wetland sites both containing natural wetlands and human-made wetlands affected by three or four factors are generally higher than that of natural wetland sites. Wetland sites in the UK and Ireland are least affected among all countries. Wetland management plans in different regions still have large space for improvement, especially in Africa and Asia. The protection and restoration of global wetlands can be carried out in five aspects, including management and policy, monitoring, restoration, knowledge, and funding.

Corporate Citizenship: An Institutional Tool To Gauge Stakeholders Participation in Holy Name University, Tagbilaran City, Bohol, Philippines

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Ernesto O. Golosino
Keyword(s):  
Corporate Citizenship ◽  
Survey Method ◽  
Questionnaire Data ◽  
Think Tank ◽  
Data Gaps ◽  
Key Informant ◽  
High Regard ◽  
Almost All ◽  
Key Informant Interview ◽  
Descriptive Survey

The advent of modernization and cutthroat competition necessitates an organization to dismantle if not redefine its directions and key positions in the market. This compelling reality prompted many corporations to change its vision, mission and goals and even to the point of system’s overhaul. Whatever modes adopted in order to get an edge in the market calls for participative and collegial decisions. However, strategic directions and corporate plans if conceptualized by the few “corporate think tank” only remain as beautiful as a piece of poetry. The absence of sense of ownership will convert these powerful ideas into garbage. Hence, cascading them to the entire system becomes a must. It is from this contention that this study was conceptualized. The primary aim of this study was to assess HNU’s practices in terms of corporate citizenship. The study used descriptive survey method. Personal interviews were effected in order to avoid bias and subjective interpretation of the questionnaire. Data gaps were addressed thru key informant interview. The findings showed that almost all parameters of corporate citizenship were given high regard, , but the prevalence of minute yet significant unfavorable data calls for an alarm.   Keywords - corporate citizenship, institutional tool, stakeholder’s participation

Using Eddy-Covariance to understand CO2 and CH4 flux dynamics within a temperate, coastal wetland on French Island, Victoria, Australia

2020 ◽  
Author(s):  
Matthew Peck ◽  
Ruth Reef ◽  
Nigel Tapper ◽  
Edoardo Daly ◽  
Leigh Burgess ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Eddy Covariance ◽  
Coastal Wetlands ◽  
Coastal Wetland ◽  
Climate Data ◽  
Flux Dynamics ◽  
Tropical Regions ◽  
Full Dataset ◽  
Inland Wetlands ◽  
Temperate Wetlands

&lt;p&gt;Coastal wetlands play a pivotal role in regulating both carbon (CO&lt;sub&gt;2&lt;/sub&gt;) and methane (CH&lt;sub&gt;4&lt;/sub&gt;) concentrations across the globe. The amount of CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4 &lt;/sub&gt;stored and released by these ecosystems is becoming more understood, in particular, within each aspect of the ecosystem. However, how the dynamics of the ecosystem affect CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4 &lt;/sub&gt;fluxes on a microclimate level is poorly understood, as well as the overall flux of these Greenhouse Gases (GHGs) within temperate, coastal wetlands. Current research primarily focuses on inland wetlands and coastal wetlands in sub-tropical and tropical regions. Thus, this research aims to investigate CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4 &lt;/sub&gt;fluxes within coastal, temperate wetlands, and improve the understanding of how environmental dynamics impact the flux of these critically important Greenhouse Gases (GHGs).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;To satisfy this aim, the use of the Eddy-Covariance (EC) method was employed. An EC station was installed on the South-West tip of French Island, Victoria, Australia in late February 2018. The collected data demonstrates the challenges with collecting micro-climate data in an ecosystem with ever-changing environmental conditions. The preliminary results indicate how sensitive flux dynamics are within coastal, temperate wetlands, in particular, to factors such as: tidal and seasonal inundation, seasonal vegetation dynamics, and shifting ecological gradients. The data obtained by the EC station provides a preliminary indication of the complexities of accounting for, and understanding, carbon and methane movement through coastal wetlands in general. The full dataset will aid in improving this understanding, specifically for rare, temperate wetland environments, increasing the knowledge base on how flux dynamics of carbon and methane are affected when collected via open-source methods in dynamic environments.&lt;/p&gt;

scholarly journals Impacts of climate and reclamation on temporal variations in CH<sub>4</sub> emissions from different wetlands in China: from 1950 to 2010

2015 ◽  
Vol 12 (23) ◽  
pp. 6853-6868 ◽  
Author(s):  
T. Li ◽  
W. Zhang ◽  
Q. Zhang ◽  
Y. Lu ◽  
G. Wang ◽  
...  
Keyword(s):  
Climate Warming ◽  
Coastal Wetlands ◽  
Temporal Variations ◽  
Tibet Plateau ◽  
Atmospheric Methane ◽  
Temporal And Spatial Variations ◽  
Ch4 Emissions ◽  
Natural Wetlands ◽  
Temporal And Spatial ◽  
Qinghai Tibet Plateau

Abstract. Natural wetlands are among the most important sources of atmospheric methane and thus important for better understanding the long-term temporal variations in the atmospheric methane concentration. During the last 60 years, wetlands have experienced extensive conversion and impacts from climate warming which might result in complicated temporal and spatial variations in the changes of the wetland methane emissions. In this paper, we present a modeling framework, integrating CH4MODwetland, TOPMODEL, and TEM models, to analyze the temporal and spatial variations in CH4 emissions from natural wetlands (including inland marshes/swamps, coastal wetlands, lakes, and rivers) in China. Our analysis revealed a total increase of 25.5 %, averaging 0.52 g m−2 per decade, in the national CH4 fluxes from 1950 to 2010, which was mainly induced by climate warming. Larger CH4 flux increases occurred in northeastern, northern, and northwestern China, where there have been higher temperature rises. However, decreases in precipitation due to climate warming offset the increment of CH4 fluxes in these regions. The CH4 fluxes from the wetland on the Qinghai–Tibet Plateau exhibited the lowest CH4 increase (0.17 g m−2 per decade). Although climate warming has accelerated CH4 fluxes, the total amount of national CH4 emissions decreased by approximately 2.35 Tg (1.91–2.81 Tg), i.e., from 4.50 Tg in the early 1950s to 2.15 Tg in the late 2000s, due to the wetland loss totalling 17.0 million ha. Of this reduction, 0.26 Tg (0.24–0.28 Tg) was derived from lakes and rivers, 0.16 Tg (0.13–0.20 Tg) from coastal wetlands, and 1.92 Tg (1.54–2.33 Tg) from inland wetlands. Spatially, northeastern China contributed the most to the total reduction, with a loss of 1.68 Tg. The wetland CH4 emissions reduced by more than half in most regions in China except for the Qinghai–Tibet Plateau, where the CH4 decrease was only 23.3 %.

scholarly journals Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2020 ◽  
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

scholarly journals Understanding and mitigating the impact of data gaps on offshore wind resource estimates

2021 ◽  
Vol 6 (2) ◽  
pp. 505-520
Author(s):  
Julia Gottschall ◽  
Martin Dörenkämper
Keyword(s):  
Wind Energy ◽  
Mesoscale Model ◽  
Measurement Data ◽  
Reanalysis Data ◽  
Offshore Wind ◽  
Data Gaps ◽  
Mean Wind Speed ◽  
Harsh Climate ◽  
Almost All ◽  
The Impact

Abstract. Like almost all measurement datasets, wind energy siting data are subject to data gaps that can for instance originate from a failure of the measurement devices or data loggers. This is in particular true for offshore wind energy sites where the harsh climate can restrict the accessibility of the measurement platform, which can also lead to much longer gaps than onshore. In this study, we investigate the impact of data gaps, in terms of a bias in the estimation of siting parameters and its mitigation by correlation and filling with mesoscale model data. Investigations are performed for three offshore sites in Europe, considering 2 years of parallel measurement data at the sites, and based on typical wind energy siting statistics. We find a mitigation of the data gaps' impact, i.e. a reduction of the observed biases, by a factor of 10 on mean wind speed, direction and Weibull scale parameter and a factor of 3 on Weibull shape parameter. With increasing gap length, the gaps' impact increases linearly for the overall measurement period while this behaviour is more complex when investigated in terms of seasons. This considerable reduction of the impact of the gaps found for the statistics of the measurement time series almost vanishes when considering long-term corrected data, for which we refer to 30 years of reanalysis data.

scholarly journals Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2021 ◽  
Vol 13 (5) ◽  
pp. 2001-2023
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Amazon Basin ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area are a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary in their wetland definition, causing substantial disagreement between and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed the global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset at a ∼ 25 km resolution at the Equator (0.25∘) at a monthly time step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at a coarse resolution with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We excluded all permanent water bodies (e.g., lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0×106 km2 (13.0 Mkm2), which can be divided into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M shows good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Basin lowlands and West Siberian lowlands, with Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetland products. By evaluating the temporal variation in WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño–Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at https://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

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