scholarly journals Multi-source global wetland mapping: combining surface water imagery and groundwater constraints

2018 ◽  
Author(s):  
Ardalan Tootchi ◽  
Anne Jost ◽  
Agnès Ducharne
Keyword(s):  
Surface Area ◽  
Land Surface ◽  
Large Scale ◽  
Soil Surface ◽  
Open Water ◽  
Wetland Classification ◽  
Wetland Mapping ◽  
Coastal Zones ◽  
Land Area ◽  
Shallow Water Table

Abstract. Wetlands are important players in the Earth climate system because of their effect on ecosystems, river discharge, water quality, and through their feedback effects on atmosphere by increasing methane emission and evapotranspiration. Many datasets have been developed for open water and wetland mapping, based on three main methods: (i) compiling national/regional wetland maps; (ii) identifying inundated areas by satellite imagery; (iii) delineating wetlands as areas with shallow water table depths. There is a massive disagreement, however, between the resulting wetland extent estimates (from 3 to 21 % of the land surface area). To reconcile these differences, we propose composite wetland (CW) maps consisting of two classes of wetlands: (1) regularly flooded wetlands (RFWs) which are obtained by overlapping selected open-water and inundation datasets, and cover 9.7 % of the land surface area; (2) scattered groundwater wetlands (SGWs), derived either from direct groundwater modelling or simplified modelling based on the topographic index (TI), using several variants. In this framework, wetlands are defined as zones that are either inundated or where the groundwater is sufficiently close to the surface to maintain near saturated soil surface. By combining RFW and different SGW maps, seven CW maps are generated, which correspond to contemporary potential wetlands, i.e. the areas that would turn into actual wetlands under the present climate assuming no human influence. They are produced at the 15 arc-sec resolution (almost 500 m at the Equator) using geographic information system (GIS) tools. Two CW maps, showing the best overall match with the available evaluation datasets, are eventually selected. Wetlands in these maps respectively cover 21.1 and 21.6 % of the global land area, which is in the high end of the literature range, along with recent estimates also recognizing the contribution of groundwater-driven wetlands. The two proposed composite maps agree massively about six major wetland hotspots, which include 75 % of the global wetlands, and concentrate in the boreal, tropical, and coastal zones. The high wetland density in the tropics is brought by the SGWs, which allows detecting wetlands under dense canopy and cloud cover. Another major feature of the two CW maps, brought by the SGWs and the high resolution of the maps, is the identification of many small and scattered wetlands, which cover less than 5 % of the land area, but are very important for hydrological and ecological functioning in temperate to arid areas. By distinguishing the RFWs and SGWs globally based on uniform principles, we eventually propose a simple wetland classification focused on hydrologic functioning, believed to be very useful for large-scale land surface modelling.

scholarly journals Multi-source global wetland maps combining surface water imagery and groundwater constraints

2019 ◽  
Vol 11 (1) ◽  
pp. 189-220 ◽  
Author(s):  
Ardalan Tootchi ◽  
Anne Jost ◽  
Agnès Ducharne
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Environmental Planning ◽  
Groundwater Modeling ◽  
Shallow Groundwater ◽  
Open Water ◽  
Land Area ◽  
Shallow Water Table ◽  
Global Land ◽  
Wetland Class

Abstract. Many maps of open water and wetlands have been developed based on three main methods: (i) compiling national and regional wetland surveys, (ii) identifying inundated areas via satellite imagery and (iii) delineating wetlands as shallow water table areas based on groundwater modeling. However, the resulting global wetland extents vary from 3 % to 21 % of the land surface area because of inconsistencies in wetland definitions and limitations in observation or modeling systems. To reconcile these differences, we propose composite wetland (CW) maps, combining two classes of wetlands: (1) regularly flooded wetlands (RFWs) obtained by overlapping selected open-water and inundation datasets; and (2) groundwater-driven wetlands (GDWs) derived from groundwater modeling (either direct or simplified using several variants of the topographic index). Wetlands are statically defined as areas with persistent near-saturated soil surfaces because of regular flooding or shallow groundwater, disregarding most human alterations (potential wetlands). Seven CW maps were generated at 15 arcsec resolution (ca. 500 m at the Equator) using geographic information system (GIS) tools and by combining one RFW and different GDW maps. To validate this approach, these CW maps were compared with existing wetland datasets at the global and regional scales. The spatial patterns were decently captured, but the wetland extents were difficult to assess compared to the dispersion of the validation datasets. Compared with the only regional dataset encompassing both GDWs and RFWs, over France, the CW maps performed well and better than all other considered global wetland datasets. Two CW maps, showing the best overall match with the available evaluation datasets, were eventually selected. These maps provided global wetland extents of 27.5 and 29 million km2, i.e., 21.1 % and 21.6 % of the global land area, which are among the highest values in the literature and are in line with recent estimates also recognizing the contribution of GDWs. This wetland class covers 15 % of the global land area compared with 9.7 % for RFW (with an overlap of ca. 3.4 %), including wetlands under canopy and/or cloud cover, leading to high wetland densities in the tropics and small scattered wetlands that cover less than 5 % of land but are highly important for hydrological and ecological functioning in temperate to arid areas. By distinguishing the RFWs and GDWs based globally on uniform principles, the proposed dataset might be useful for large-scale land surface modeling (hydrological, ecological and biogeochemical modeling) and environmental planning. The dataset consisting of the two selected CW maps and the contributing GDW and RFW maps is available from PANGAEA at https://doi.org/10.1594/PANGAEA.892657 (Tootchi et al., 2018).

scholarly journals Multi-source global wetland maps combining surface water imagery and groundwater constraints

2018 ◽  
Author(s):  
Ardalan Tootchi ◽  
Anne Jost ◽  
Agnès Ducharne
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Environmental Planning ◽  
Shallow Groundwater ◽  
Open Water ◽  
Groundwater Modelling ◽  
Land Area ◽  
Shallow Water Table ◽  
Global Land ◽  
Wetland Class

Abstract. Many maps of open water and wetlands have been developed based on three main methods: (i) compiling national/regional wetland surveys; (ii) identifying inundated areas via satellite imagery; and (iii) delineating wetlands as shallow water table areas based on groundwater modelling. However, the resulting global wetland extents vary from 3 % to 21 % of the land surface area because of inconsistencies in wetland definitions and limitations in observation or modelling systems. To reconcile these differences, we propose composite wetland (CW) maps, combining two classes of wetlands: (1) regularly flooded wetlands (RFW) obtained by overlapping selected open-water and inundation datasets; and (2) groundwater-driven wetlands (GDW) derived from groundwater modelling (either direct or simplified using several variants of the topographic index). Wetlands are statically defined as areas with persistent near-saturated soil surfaces because of regular flooding or shallow groundwater. Seven CW maps were generated at the 15 arc-sec resolution (ca 500 m at the Equator) using geographic information system (GIS) tools and by combining one RFW and different GDW maps. To validate this approach, these CW maps were compared with existing wetland datasets at the global and regional scales. The spatial patterns were decently captured, but the wetland extents were difficult to assess against the dispersion of the validation datasets. Compared with the only regional dataset encompassing both GDWs and RFWs, over France, the CW maps performed well and better than all other considered global wetland datasets. Two CW maps, showing the best overall match with the available evaluation datasets, were eventually selected. These maps provided global wetland extents of 27.5 and 29 million km², i.e., 21.1 % and 21.6 % of global land area, which are among the highest values in the literature and in line with recent estimates also recognizing the contribution of GDWs. This wetland class covers 15 % of the global land area compared with 9.7 % for RFW (with an overlap of ca. 3.4 %), including wetlands under canopy/cloud cover, leading to high wetland densities in the tropics and small scattered wetlands that cover less than 5 % of land but are highly important for hydrological and ecological functioning in temperate to arid areas. By distinguishing the RFWs and GDWs based globally on uniform principles, the proposed dataset might be useful for large-scale land surface modelling (hydrological, ecological and biogeochemical modelling) and environmental planning. The dataset consisting of the two selected CW maps and the contributing GDW and RFW maps is available from PANGAEA at https://doi.pangaea.de/10.1594/PANGAEA.892657

scholarly journals Comparison of Water-Related Land Cover Types in Six 1-km Global Land Cover Datasets

2012 ◽  
Vol 13 (2) ◽  
pp. 649-664 ◽  
Author(s):  
Tosiyuki Nakaegawa
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Human Impacts ◽  
Open Water ◽  
Land Cover Classification ◽  
Wetland Classification ◽  
Land Cover Types ◽  
Global Land Cover ◽  
Global Land ◽  
Snow And Ice

Abstract Land cover classification is a fundamental and vital activity that is helpful for understanding natural dynamics and the human impacts of land surface processes. Available multiple 1-km global land cover datasets have been compared to identify classification accuracy and uncertainties for vegetation land cover types, but they have not been adequately compared for water-related land cover types. Six 1-km global land cover datasets were comprehensively examined by focusing on three water-related land cover types (snow and ice, wetlands, and open water). The global mean per-pixel agreement measured by the class-specific consistency is high for snow and ice, medium for open water, and low for wetlands. The agreement is low for snow and ice in low latitudes and high for open water and snow and ice in high latitudes. Areas classified as wetlands in a pixel in one dataset are rarely classified as wetlands in the same pixel in the other five datasets. These areas are most often classified as forest, wetland, or shrub. Areas of snow and ice and open water in some regions are not always chronologically consistent among the datasets because nonsatellite data and different algorithms are used to determine the areas. Further research is necessary to reduce uncertainty in the water-related land cover classification and to develop an advanced classification algorithm that can detect water under a vegetation canopy for improvement in wetland classification. Chronological inconsistency between 1-km land cover datasets and satellite observation periods must also be addressed.

scholarly journals Thermal Anomalies Detect Critical Global Land Surface Changes

2018 ◽  
Vol 57 (2) ◽  
pp. 391-411 ◽  
Author(s):  
D. J. Mildrexler ◽  
M. Zhao ◽  
W. B. Cohen ◽  
S. W. Running ◽  
X. P. Song ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Large Scale ◽  
Heat Waves ◽  
Land Cover Changes ◽  
Land Area ◽  
Thermal Anomalies ◽  
Extreme Climatic Events ◽  
Global Land ◽  
Surface Changes

AbstractMeasurements that link surface conditions and climate can provide critical information on important biospheric changes occurring in the Earth system. As the direct driving force of energy and water fluxes at the surface–atmosphere interface, land surface temperature (LST) provides information on physical processes of land-cover change and energy-balance changes that air temperature cannot provide. Annual maximum LST (LSTmax) is especially powerful at minimizing synoptic and seasonal variability and highlighting changes associated with extreme climatic events and significant land-cover changes. The authors investigate whether maximum thermal anomalies from satellite observations could detect heat waves and droughts, a melting cryosphere, and disturbances in the tropical forest from 2003 to 2014. The 1-km2 LSTmax anomalies peaked in 2010 when 20% of the global land area experienced anomalies of greater than 1 standard deviation and over 4% of the global land area was subject to positive anomalies exceeding 2 standard deviations. Positive LSTmax anomalies display complex spatial patterns associated with heat waves and droughts across the global land area. The findings presented herein show that entire biomes are experiencing shifts in their LSTmax distributions driven by extreme climatic events and large-scale land surface changes, such as melting of ice sheets, severe droughts, and the incremental effects of forest loss in tropical forests. As climate warming and land-cover changes continue, it is likely that Earth’s maximum surface temperatures will experience greater and more frequent directional shifts, increasing the possibility that critical thresholds in Earth’s ecosystems and climate system will be surpassed, resulting in profound and irreversible changes.

scholarly journals InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3315
Author(s):  
Meisam Amani ◽  
Valentin Poncos ◽  
Brian Brisco ◽  
Fatemeh Foroughnia ◽  
Evan R. DeLancey ◽  
...  
Keyword(s):  
Open Water ◽  
Added Value ◽  
Wetland Classification ◽  
Interferometric Synthetic Aperture Radar ◽  
Wetland Mapping ◽  
Change Analysis ◽  
Future Studies ◽  
Multi Temporal ◽  
Trends Over Time ◽  
Reference Samples

Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that have investigated the application of the Interferometric Synthetic Aperture Radar (InSAR) coherence products for wetland studies, especially over large areas. Therefore, in this study, coherence products over the entire province of Alberta, Canada (~661,000 km2) were generated using the Sentinel-1 data acquired from 2017 to 2020. Then, these products along with large amount of wetland reference samples were employed to assess the separability of different wetland types and their trends over time. Overall, our analyses showed that coherence can be considered as an added value feature for wetland classification and monitoring. The Treed Bog and Shallow Open Water classes showed the highest and lowest coherence values, respectively. The Treed Wetland and Open Wetland classes were easily distinguishable. When analyzing the wetland subclasses, it was observed that the Treed Bog and Shallow Open Water classes can be easily discriminated from other subclasses. However, there were overlaps between the signatures of the other wetland subclasses, although there were still some dates where these classes were also distinguishable. The analysis of multi-temporal coherence products also showed that the coherence products generated in spring/fall (e.g., May and October) and summer (e.g., July) seasons had the highest and lowest coherence values, respectively. It was also observed that wetland classes preserved coherence during the leaf-off season (15 August–15 October) while they had relatively lower coherence during the leaf-on season (i.e., 15 May–15 August). Finally, several suggestions for future studies were provided.

scholarly journals Towards a Strategy to Implement the China Wetland Mapping Using Landsat TM

2017 ◽  
Author(s):  
Haiying Zhang ◽  
Zhenguo Niu ◽  
Yaomin Zheng
Keyword(s):  
Large Scale ◽  
Spectral Characteristics ◽  
River Estuary ◽  
Landsat Tm ◽  
Classification Model ◽  
Wetland Classification ◽  
Wetland Mapping ◽  
Visual Interpretation ◽  
Ancillary Data ◽  
The Sanjiang Plain

Wetlands are among the most bio-diverse and highest productivity ecosystems on earth, making their monitoring a high priority to conservation, protection and management interests. Although visual interpretation of satellite images is generally precise for monitoring wetlands, recent works have emphasized computerized classification methods because of the reduction in analyst time. However, it is difficult to automatically identify wetland solely based on spectral characteristics due to the complexity of wetland ecosystems. The ability to extract wetland information rapidly and accurately is the basis and the key to wetland mapping at a large scale. Here we propose an operational method to map China wetlands based on Landsat TM data and ancillary data. On the basis of theoretical analysis of wetland automatic classification, we developed a revised multi-layer wetland classification scheme and a rule-based classification model. In the latter, supervised classification (SVM and decision tree) and unsupervised classification (ISODATA) methods were tested. Four Landsat TM images, representing various wetland eco-regions in China (i.e. the Sanjiang Plain in the northeast China, the North China Plain, the Zoige Plateau in the southwest China and the Pearl River Estuary in southeast China), were automatically classified. The overall classification accuracies were 86.57%, 96.00%, 84.51% and 88.30%, respectively, which we considered to be satisfactory accuracy. Our results indicate that issues such as the resolution of geographic data and the understanding of wetland samples should be carefully addressed in the future.

Dissolved Phosphorus Losses in Tile Drainage under Subirrigation

2006 ◽  
Vol 41 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Nicolas Stämpfli ◽  
Chandra A. Madramootoo
Keyword(s):  
Water Table ◽  
Residual Effect ◽  
Surface Water Quality ◽  
Soil Surface ◽  
Quality Standard ◽  
Tile Drainage ◽  
Agricultural Field ◽  
Shallow Water Table ◽  
Dissolved Phosphorus ◽  
Water Table Control

Abstract Recent studies have shown subirrigation (SI) to be effective in reducing nitrate losses from agricultural tile drainage systems. A field study was conducted from 2001 to 2002 in southwestern Québec to evaluate the effect of SI on total dissolved phosphorus (TDP) losses in tile drainage. In an agricultural field with drains installed at a 1-m depth, a SI system with a design water table depth (WTD) of 0.6 m below the soil surface was compared with conventional free drainage (FD). Subirrigation increased drainage outflow volumes in the autumn, when drains were opened and water table control was interrupted for the winter in the SI plots. Outflows were otherwise similar for both treatments. Throughout the study, the TDP concentrations in tile drainage were significantly higher with SI than with FD for seven out of 17 of the sampling dates for which data could be analyzed statistically, and they were never found to be lower for plots under SI than for plots under FD. Of the seven dates for which the increase was significant, six fell in the period during which water table control was not implemented (27 September 2001 to 24 June 2002). Hence, it appears that SI tended to increase TDP concentrations compared with FD, and that it also had a residual effect between growing seasons. Almost one-third of all samples from the plots under SI exceeded Québec's surface water quality standard (0.03 mg TDP L-1), whereas concentrations in plots under FD were all below the standard. Possible causes of the increase in TDP concentrations in tile drainage with SI are high TDP concentrations found in the well water used for SI and a higher P solubility caused by the shallow water table.

scholarly journals Reservoir-Induced Land Deformation: Case Study from the Grand Ethiopian Renaissance Dam

2021 ◽  
Vol 13 (5) ◽  
pp. 874
Author(s):  
Yu Chen ◽  
Mohamed Ahmed ◽  
Natthachet Tangdamrongsub ◽  
Dorina Murgulet
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Surface Deformation ◽  
Vertical Displacement ◽  
Nile River ◽  
Reservoir Volume ◽  
Novel Approach ◽  
Land Deformation ◽  
Large Scale Land ◽  
The Impact

The Nile River stretches from south to north throughout the Nile River Basin (NRB) in Northeast Africa. Ethiopia, where the Blue Nile originates, has begun the construction of the Grand Ethiopian Renaissance Dam (GERD), which will be used to generate electricity. However, the impact of the GERD on land deformation caused by significant water relocation has not been rigorously considered in the scientific research. In this study, we develop a novel approach for predicting large-scale land deformation induced by the construction of the GERD reservoir. We also investigate the limitations of using the Gravity Recovery and Climate Experiment Follow On (GRACE-FO) mission to detect GERD-induced land deformation. We simulated three land deformation scenarios related to filling the expected reservoir volume, 70 km3, using 5-, 10-, and 15-year filling scenarios. The results indicated: (i) trends in downward vertical displacement estimated at −17.79 ± 0.02, −8.90 ± 0.09, and −5.94 ± 0.05 mm/year, for the 5-, 10-, and 15-year filling scenarios, respectively; (ii) the western (eastern) parts of the GERD reservoir are estimated to move toward the reservoir’s center by +0.98 ± 0.01 (−0.98 ± 0.01), +0.48 ± 0.00 (−0.48 ± 0.00), and +0.33 ± 0.00 (−0.33 ± 0.00) mm/year, under the 5-, 10- and 15-year filling strategies, respectively; (iii) the northern part of the GERD reservoir is moving southward by +1.28 ± 0.02, +0.64 ± 0.01, and +0.43 ± 0.00 mm/year, while the southern part is moving northward by −3.75 ± 0.04, −1.87 ± 0.02, and −1.25 ± 0.01 mm/year, during the three examined scenarios, respectively; and (iv) the GRACE-FO mission can only detect 15% of the large-scale land deformation produced by the GERD reservoir. Methods and results demonstrated in this study provide insights into possible impacts of reservoir impoundment on land surface deformation, which can be adopted into the GERD project or similar future dam construction plans.

scholarly journals Hydrology influences breeding time in the white-throated dipper

BMC Ecology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Anna L. K. Nilsson ◽  
Thomas Skaugen ◽  
Trond Reitan ◽  
Jan Henning L’Abée-Lund ◽  
Marlène Gamelon ◽  
...  
Keyword(s):  
Climate Change ◽  
River Discharge ◽  
Large Scale ◽  
Time Window ◽  
River System ◽  
Open Water ◽  
Local Conditions ◽  
Environmental Indices ◽  
Groundwater Levels ◽  
Breeding Time

Abstract Background Earlier breeding is one of the strongest responses to global change in birds and is a key factor determining reproductive success. In most studies of climate effects, the focus has been on large-scale environmental indices or temperature averaged over large geographical areas, neglecting that animals are affected by the local conditions in their home ranges. In riverine ecosystems, climate change is altering the flow regime, in addition to changes resulting from the increasing demand for renewable and clean hydropower. Together with increasing temperatures, this can lead to shifts in the time window available for successful breeding of birds associated with the riverine habitat. Here, we investigated specifically how the environmental conditions at the territory level influence timing of breeding in a passerine bird with an aquatic lifestyle, the white-throated dipper Cinclus cinclus. We relate daily river discharge and other important hydrological parameters, to a long-term dataset of breeding phenology (1978–2015) in a natural river system. Results Dippers bred earlier when winter river discharge and groundwater levels in the weeks prior to breeding were high, and when there was little snow in the catchment area. Breeding was also earlier at lower altitudes, although the effect dramatically declined over the period. This suggests that territories at higher altitudes had more open water in winter later in the study period, which permitted early breeding also here. Unexpectedly, the largest effect inducing earlier breeding time was territory river discharge during the winter months and not immediately prior to breeding. The territory river discharge also increased during the study period. Conclusions The observed earlier breeding can thus be interpreted as a response to climate change. Measuring environmental variation at the scale of the territory thus provides detailed information about the interactions between organisms and the abiotic environment.

Sign in / Sign up

Export Citation Format

Share Document