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 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 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 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.

Revealing the spatial pattern of subsurface soil salinity over the Argentinean Dry Chaco

2021 ◽  
Author(s):  
Michiel Maertens ◽  
Veerle Vanacker ◽  
Gabriëlle De Lannoy ◽  
Frederike Vincent ◽  
Raul Giménez ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Shallow Groundwater ◽  
Soil Salinization ◽  
Dry Forest ◽  
Surface Model ◽  
Subsurface Soil

<p>The South-American Dry Chaco is a unique ecoregion as it is one of the largest sedimentary plains in the world hosting the planet’s largest dry forest. The 787.000 km² region covers parts of Argentina, Paraguay, and Bolivia and is characterized by a negative climatic water balance as a consequence of limited rainfall inputs (800 mm/year) and high temperatures (21°C). In combination with the region’s extreme flat topography (slopes < 0.1%) and shallow groundwater tables, saline soils are expected in substantial parts of the region. In addition, it is expected that large-scale deforestation processes disrupt the hydrological cycle resulting in rising groundwater tables and further increase the risk for soil salinization.</p><p>In this study, we identified the regional-scale patterns of subsurface soil salinity in the Dry Chaco.  Field data were obtained during a two-month field campaign in the dry season of 2019. A total of 492 surface- and 142 subsurface-samples were collected along East-West transects to determine soil electric conductivity, pH, bulk density and humidity. Spatial regression techniques were used to reveal the topographic and ecohydrological variables that are associated with subsurface soil salinity over the Dry Chaco. The hydrological information was obtained from a state-of-the-art land surface model with an improved set of satellite-derived vegetation and land cover parameters.</p><p>In the presentation, we will present a subsurface soil salinity map for a part of the Argentinean Dry Chaco and provide relevant insights into the driving mechanisms behind it.</p>

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 Estimation of Groundwater Temperatures in Paris, France

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Hannes Hemmerle ◽  
Sina Hale ◽  
Ingo Dressel ◽  
Susanne A. Benz ◽  
Guillaume Attard ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Mean Squared Error ◽  
Shallow Groundwater ◽  
Estimation Procedure ◽  
Temperature Data ◽  
Groundwater Temperature ◽  
Surface Temperatures ◽  
Land Surface Temperatures ◽  
The City

Subsurface temperature data is usually only accessible as point information with a very limited number of observations. To spatialize these isolated insights underground, we usually rely on interpolation methods. Unfortunately, these conventional tools are in many cases not suitable to be applied to areas with high local variability, like densely populated areas, and in addition are very vulnerable to uneven distributions of wells. Since thermal conditions of the surface and shallow subsurface are coupled, we can utilize this relationship to estimate shallow groundwater temperatures from satellite-derived land surface temperatures. Here, we propose an estimation approach that provides spatial groundwater temperature data and can be applied to natural, urban, and mixed environments. To achieve this, we combine land surface temperatures with anthropogenic and natural processes, such as downward heat transfer from buildings, insulation through snow coverage, and latent heat flux in the form of evapotranspiration. This is demonstrated for the city of Paris, where measurements from as early as 1977 reveal the existence of a substantial subsurface urban heat island (SUHI) with a maximum groundwater temperature anomaly of around 7 K. It is demonstrated that groundwater temperatures in Paris can be well predicted with a root mean squared error of below 1 K by means of satellite-derived land surface images. This combined approach is shown to improve existing estimation procedures that are focused either on rural or on urban conditions. While they do not detect local hotspots caused by small-scaled heat sources located underground (e.g., sewage systems and tunnels), the findings for the city of Paris for the estimation of large-scale thermal anomalies in the subsurface are promising. Thus, the new estimation procedure may also be suitable for other cities to obtain a more reliable insight into the spatial distribution of urban ground and groundwater temperatures.

scholarly journals Implementing northern peatlands in a global land surface model: description and evaluation in the ORCHIDEE high-latitude version model (ORC-HL-PEAT)

2018 ◽  
Vol 11 (8) ◽  
pp. 3279-3297 ◽  
Author(s):  
Chloé Largeron ◽  
Gerhard Krinner ◽  
Philippe Ciais ◽  
Claire Brutel-Vuilmet
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Water Storage ◽  
Land Surface Model ◽  
Model Description ◽  
High Water Content ◽  
Surface Model ◽  
Northern Latitudes ◽  
Global Land ◽  
Northern Peatlands

Abstract. Widely present in boreal regions, peatlands contain large carbon stocks because of their hydrologic properties and high water content, which makes primary productivity exceed decomposition rates. We have enhanced the global land surface model ORCHIDEE by introducing a hydrological representation of northern peatlands. These peatlands are represented as a new plant functional type (PFT) in the model, with specific hydrological properties for peat soil. In this paper, we focus on the representation of the hydrology of northern peatlands and on the evaluation of the hydrological impact of this implementation. A prescribed map based on the inventory of Yu et al. (2010) defines peatlands as a fraction of a grid cell represented as a PFT comparable to C3 grasses, with adaptations to reproduce shallow roots and higher photosynthesis stress. The treatment of peatland hydrology differs from that of other vegetation types by the fact that runoff from other soil types is partially directed towards the peatlands (instead of directly to the river network). The evaluation of this implementation was carried out at different spatial and temporal scales, from site evaluation to larger scales such as the watershed scale and the scale of all northern latitudes. The simulated net ecosystem exchanges agree with observations from three FLUXNET sites. Water table positions were generally close to observations, with some exceptions in winter. Compared to other soils, the simulated peat soils have a reduced seasonal variability in water storage. The seasonal cycle of the simulated extent of inundated peatlands is compared to flooded area as estimated from satellite observations. The model is able to represent more than 89.5 % of the flooded areas located in peatland areas, where the modelled extent of inundated peatlands reaches 0.83×106 km2. However, the extent of peatlands in northern latitudes is too small to substantially impact the large-scale terrestrial water storage north of 45∘ N. Therefore, the inclusion of peatlands has a weak impact on the simulated river discharge rates in boreal regions.

Large-Scale Runoff from Landmasses: A Global Assessment of the Closure of the Hydrological and Atmospheric Water Balances*

2014 ◽  
Vol 15 (6) ◽  
pp. 2111-2139 ◽  
Author(s):  
Christof Lorenz ◽  
Harald Kunstmann ◽  
Balaji Devaraju ◽  
Mohammad J. Tourian ◽  
Nico Sneeuw ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Global Precipitation Climatology Project ◽  
Research Unit ◽  
Climatic Conditions ◽  
Climatic Research Unit ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Land ◽  
Global Precipitation

Abstract The performance of hydrological and hydrometeorological water-balance-based methods to estimate monthly runoff is analyzed. Such an analysis also allows for the examination of the closure of water budgets at different spatial (continental and catchment) and temporal (monthly, seasonal, and annual) scales. For this analysis, different combinations of gridded observations [Global Precipitation Climatology Centre (GPCC), Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC), Climatic Research Unit (CRU), and University of Delaware (DEL)], atmospheric reanalysis models [Interim ECMWF Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)], partially model-based datasets [Global Land Surface Evaporation: The Amsterdam Methodology (GLEAM), Moderate Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16), and FLUXNET Multi-Tree Ensemble (FLUXNET MTE)], and Gravity Recovery and Climate Experiment (GRACE) satellite-derived water storage changes are employed. The derived ensemble of hydrological and hydrometeorological budget–based runoff estimates, together with results from different land surface hydrological models [Global Land Data Assimilation System (GLDAS) and the land-only version of MERRA (MERRA-Land)] and a simple predictor based on the precipitation–runoff ratio, is compared with observed monthly in situ runoff for 96 catchments of different sizes and climatic conditions worldwide. Despite significant shortcomings of the budget-based methods over many catchments, the evaluation allows for the demarcation of areas with consistently reasonable runoff estimates. Good agreement was particularly observed when runoff followed a dominant annual cycle like the Amazon. This holds true also for catchments with an area far below the spatial resolution of GRACE, like the Rhine. Over catchments with low or nearly constant runoff, the budget-based approaches do not provide realistic runoff estimates because of significant biases in the input datasets. In general, no specific data combination could be identified that consistently performed over all catchments. Thus, the performance over a specific single catchment cannot be extrapolated to other regions. Only in few cases do specific dataset combinations provide reasonable water budget closure; in most cases, significant imbalances remain for all the applied datasets.

scholarly journals Effects of bryophyte and lichen cover on permafrost soil temperature at large scale

2016 ◽  
Vol 10 (5) ◽  
pp. 2291-2315 ◽  
Author(s):  
Philipp Porada ◽  
Altug Ekici ◽  
Christian Beer
Keyword(s):  
Thermal Properties ◽  
Soil Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Dynamic Properties ◽  
Cooling Effect ◽  
Permafrost Soil ◽  
Surface Model ◽  
Lichen Cover ◽  
Global Land

Abstract. Bryophyte and lichen cover on the forest floor at high latitudes exerts an insulating effect on the ground. In this way, the cover decreases mean annual soil temperature and can protect permafrost soil. Climate change, however, may change bryophyte and lichen cover, with effects on the permafrost state and related carbon balance. It is, therefore, crucial to predict how the bryophyte and lichen cover will react to environmental change at the global scale. To date, current global land surface models contain only empirical representations of the bryophyte and lichen cover, which makes it impractical to predict the future state and function of bryophytes and lichens. For this reason, we integrate a process-based model of bryophyte and lichen growth into the global land surface model JSBACH (Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg). The model simulates bryophyte and lichen cover on upland sites. Wetlands are not included. We take into account the dynamic nature of the thermal properties of the bryophyte and lichen cover and their relation to environmental factors. Subsequently, we compare simulations with and without bryophyte and lichen cover to quantify the insulating effect of the organisms on the soil. We find an average cooling effect of the bryophyte and lichen cover of 2.7 K on temperature in the topsoil for the region north of 50° N under the current climate. Locally, a cooling of up to 5.7 K may be reached. Moreover, we show that using a simple, empirical representation of the bryophyte and lichen cover without dynamic properties only results in an average cooling of around 0.5 K. This suggests that (a) bryophytes and lichens have a significant impact on soil temperature in high-latitude ecosystems and (b) a process-based description of their thermal properties is necessary for a realistic representation of the cooling effect. The advanced land surface scheme, including a dynamic bryophyte and lichen model, will be the basis for an improved future projection of land–atmosphere heat and carbon exchange.

Links between global terrestrial water storage and large-scale modes of climatic variability

2020 ◽  
Author(s):  
Tiewei Li
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Water Cycle ◽  
Southern Oscillation ◽  
Global Climate ◽  
Climatic Variability ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Global Land ◽  
Terrestrial Water

<p>Large-scale modes of climatic variability, or teleconnections, influence global patterns of climate variability and provide a framework for understanding complex responses of the global water cycle to global climate. Here, we examine how Terrestrial Water Storage (TWS) responds to 14 major teleconnections (TCs) during the 2003–2016 period based on data obtained from the Gravity Recovery and Climate Experiment (GRACE). By examining correlations between the teleconnections and TWS anomalies (TWSA) data, we find these teleconnections significantly influence TWSA over more than 80.8% of the global land surface. The El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO) are significantly correlated with TWSA variations in 55.8%，56.2% and 60% the global land surface, while other teleconnections affect TWSA at regional scales. We also explore the TCs’ effect on three key hydrological components, including precipitation (P), evapotranspiration (ET) and runoff (R), and their contribution to TWSA variations in 225 river basins. It’s found the TCs generally exert the comprehensive but not equally impact on all three components (P, ET and R). Our findings demonstrate a significant and varying effect of multiple TCs in terrestrial hydrological balance.</p>

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