the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 May 2020
Data for wetlandscapes and their changes around the world
Geography and associated hydrological, hydroclimate and land-useconditions and their changes determine the states and dynamics of wetlandsand their ecosystem services. The influences of these controls are notlimited to just the local scale of each individual wetland but extend overlarger landscape areas that integrate multiple wetlands and their totalhydrological catchment – the wetlandscape. However, the data and knowledgeof conditions and changes over entire wetlandscapes are still scarce,limiting the capacity to accurately understand and manage critical wetlandecosystems and their services under global change. We present a newWetlandscape Change Information Database (WetCID), consisting of geographic,hydrological, hydroclimate and land-use information and data for 27wetlandscapes around the world. This combines survey-based local informationwith geographic shapefiles and gridded datasets of large-scale hydroclimateand land-use conditions and their changes over whole wetlandscapes.Temporally, WetCID contains 30-year time series of data for mean monthlyprecipitation and temperature and annual land-use conditions. Thesurvey-based site information includes local knowledge on the wetlands,hydrology, hydroclimate and land uses within each wetlandscape and on theavailability and accessibility of associated local data. This novel database(available through PANGAEAhttps://doi.org/10.1594/PANGAEA.907398; Ghajarniaet al., 2019) can support site assessments; cross-regional comparisons; andscenario analyses of the roles and impacts of land use, hydroclimatic andwetland conditions, and changes in whole-wetlandscape functions and ecosystemservices.
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Wetlands contribute more than 20 % of the total value of global ecosystemservices (Costanza et al., 2014), while covering only a small percentage(4 %–9 %) of global land surface (Morganti et al., 2019; Zedler and Kercher,2005; Mitsch and Gosselink, 2000). Wetlands are associated with a diverserange of functions, such as water quality remediation (e.g., Chalov et al.,2017; Quin et al., 2015), regulation of soil moisture and groundwaterreplenishment (e.g., Ameli and Creed, 2019; Golden et al., 2017), floodcontrol (e.g., Quin and Destouni, 2018; Acreman and Holden, 2013), andbiodiversity conservation (e.g., Cohen et al., 2016; Mitchell et al., 2008).Through these functions, wetlands can support regional sustainability(Seifollahi-Aghmiuni et al., 2019) but are also one of the most vulnerableecosystems globally (Golden et al., 2017). For instance, human land and/orwater use developments (Destouni et al., 2013; Jaramillo and Destouni, 2015;Maneas et al., 2019) in combination with climate variability and change(Orth and Destouni, 2018; Seneviratne et al., 2006) affect large-scale waterfluxes, with impacts on wetland functions and ecosystem services. Theseimpacts extend over coupled systems of multiple wetlands and the associatedtotal hydrological catchment that integrates these, referred to as awetlandscape (Thorslund et al., 2017), with even well-intended actionstowards various sustainable development goals potentially affecting wetlandfunctions and services in different directions (Jaramillo et al., 2019). Asa consequence of various change impacts, wetland areas are now sufferingrapid and continued decline in different regions worldwide (Davidson et al.,2018; Davidson, 2014).
The scale mismatch between the existing large-scale studies of variouslandscape changes and the still mostly local wetland impact studies(Thorslund et al., 2017) creates an urgent need for comprehensive,science-based assessment of the interactions between large-scale drivers ofchange and large-scale wetland systems (Ameli and Creed, 2019; Creed et al.,2017). Adopting a wetlandscape perspective involves moving away from theindividual wetland scale to consider the large-scale functioning of thehydrologically coupled system of multiple wetlands and their surroundinglandscape. Assessments at these larger scales are needed to enable theformulation of scientific evidence-based guidance and strategies to protectwetlands under global change (Thorslund et al., 2018; Ameli and Creed,2019). The conceptual framework on wetlandscapes was developed over 30 yearsago, by Preston and Bedford (1988), but the dynamics and impacts of manylarge-scale drivers or functions at wetlandscape scales remain still largelyuninvestigated and unknown, with the interactions between large-scalehydroclimatic variability and change and wetland dynamics still beinglargely underexplored at the wetlandscape scale (Thorslund et al., 2017). Thecombination of high wetland vulnerability and rapid large-scale changessubject to major knowledge and data gaps highlights the need to synthesizeand create datasets available for evaluating change effects and feedbacks atthe scales of whole wetlandscapes.
To address this need and support large-scale studies of whole wetlandscapesin and across different parts of the world, we have created a novel database,named the Wetlandscape Change Information Database (WetCID), for 27wetlandscapes around the world and their associated geographical, wetland,hydrology, hydroclimate and land-use conditions. WetCID consists of asurvey-based collection of local information and data, combined withcompilation and synthesis of gridded large-scale datasets for a range ofrelevant hydroclimatic and land-use variables.
The remainder of this paper is structured as follows: in Sect. 2, wedescribe the methodology used in collecting, processing and summarizingdifferent datasets. In Sect. 3, we present WetCID summaries and samplefigures and maps from different components of the underlying datasets inorder to exemplify and highlight the potential of new insights that can begained from using this database as well as its limitations. In Sect. 4,we discuss data availability and the format and structure of different filesin WetCID. Based on the findings, we present some conclusions in Sect. 5.
2.1 Data acquisition
In compilation of WetCID for the 27 wetlandscapes, we employed three sourcesof primary data. These were (1) local site survey data, depicting generalcharacteristics of each wetlandscape (catchment) and its geographicalcharacteristics (including shapefiles for its spatial extent) and associatedhydrological, climate and land-use conditions and their observed or perceivedchanges; (2) gridded historical data time series of monthly precipitationand temperature from Climate Research Unit Time-Series version 4.02 (CRU_TS4.02; Harris et al., 2014); and (3) historical data of annualland cover and its changes from the NOAA-HYDE dataset provided by the NOAA'sNational Climate Data Center (Jain et al., 2013; Meiyappan and Jain, 2012).
The survey for local site data was given to researchers within theGlobal Wetland Ecohydrological Network (GWEN) (http://www.gwennetwork.se/, last access: 9 May 2020). The GWEN researchers responding to the surveyspecified the relevant wetlandscape extent (total hydrological catchmentwith wetlands) and provided boundaries in GIS format for the 27wetlandscapes, located as shown in Fig. 1. Information and data of allthree types (local survey-based, hydroclimate, land use) were collected andsynthesized for each of these wetlandscapes from all three sources (1–3).In addition to the local survey information, data on hydroclimate and land-use variables were thus also compiled from the global datasets in bothgridded and aggregated form for each wetlandscape, as described further inthe following.
Figure 1Geographical distribution of the 27 wetlandscape sites included inWetCID. The background map shows the Köppen–Geiger climateclassification system (updated by Peel et al., 2007), with the number ofwetlandscapes extended from those included in similar GWEN-site mapping byThorslund et al. (2017). The site numbering is in order of latitude fromnorth to south, covering a latitude range from 70∘ N to 25∘ S.
2.2 Site information surveys
A questionnaire for collecting local site knowledge and information on theavailability and accessibility of local data was developed during a GWENworkshop held in Santa Marta, Colombia, on 24–28 April 2018. Thequestionnaire was sent out by email after the workshop to all participatingGWEN researchers. The researchers responding to it related their answers toa specific wetlandscape in which they had active research.
The questionnaire comprised two main parts. Part 1 contained generalquestions about the geography, climate, hydrology, and wetland-relevanthuman activities and changes in the wetlandscapes. Part 2 focused on theavailability and accessibility of local site data, structured into“Hydroclimate”, “Land use” and “Other” data (see templates in the databasefiles for a full outline of the questionnaire). The collective knowledgeobtained on conditions and changes in the 27 wetlandscapes and on dataavailability–accessibility is summarized in Sect. 3.1.
To complement this local knowledge and information basis, we furtherextracted and synthesized data for the 27 wetlandscapes from relevant globalhydroclimate and land-use datasets as described below.
2.3 Hydroclimate data
The temperature and precipitation data taken from the CRU_TS4.02 global datasets (Harris et al., 2014) covered a 30-year period(1981–2010), to be consistent with the time span of existing global land-usechange data. CRU_TS4.02 provides hydroclimate data withspatial resolution of and at amonthly temporal scale. In preparing temperature and precipitation datasetsfor each wetlandscape, the gridded data within the area of the wetlandscapewere extracted from the global datasets and also spatially aggregated overthat area, based on area-weighted averaging over the grid cells covered bythe shapefile of each wetlandscape (catchment). This providedwetlandscape-specific data time series for each variable at each grid celland aggregated over the whole wetlandscape. To facilitate analyses atdifferent spatial resolutions, both the gridded and the aggregated timeseries were included in WetCID for each of the 27 wetlandscapes.
In addition to the gridded and aggregated data time series, period-specifictemperature and precipitation changes were also calculated for eachwetlandscape by dividing the total 30-year time span of the collected datainto the two 15-year periods, 1981–1995 (Per1) and 1996–2010 (Per2). Suchperiod-specific change quantification can facilitate relatively simple andstraightforward analysis of how these hydroclimatic changes correlate withand may have driven other wetlandscape changes (e.g., in runoff,evapotranspiration, wetland area) between the same time periods (Destouni etal., 2013; Jaramillo and Destouni, 2014, 2015). Absolute and relative (%)changes between these periods (AbsChng and RelChng, respectively) werecalculated from the mean annual values of temperature and precipitationduring Per1 and Per2 as
where and are average temperature(in C∘) or precipitation (in mm yr−1) over Per1 (1981–1995) andPer2 (1996–2010), respectively. Equation (1) was applied to both temperature andprecipitation data to calculate their absolute changes in eachwetlandscape, while Eq. (2) was only applied to precipitation data tocalculate the corresponding percentage change in precipitation.
2.4 Land-use data
The NOAA-HYDE dataset was used to estimate land uses and their changes ineach wetlandscape. NOAA-HYDE estimates annual changes in land-cover areaover the global land mass, starting from a base map for the year 1765. Theestimations follow a predefined pathway, determined by relevant land-use andmanagement datasets (cropland, pastureland, urbanization, timberharvesting), to obtain forest area distributions close to satellite-basedestimates of forests in recent years (Meiyappan and Jain, 2012). NOAA-HYDEdata cover the period 1770–2010 with yearly temporal resolution and spatialresolution of, from which data forthe period 1981–2010 were used for the development of this database,consistent with the hydroclimate data period described above.
The NOAA-HYDE land-cover maps show the percentage of grid cell areacontaining 28 different land-cover types (see Table A1 in Appendix A). Inthis study, we reclassified these 28 land-cover types into 10 distinct land-cover types, namely urban, shrubland, grassland, pastureland, cropland, forest, water,desert, tundra and savannah, by combining similar land-cover classes (seeTable A1). As done for the hydroclimate data, the gridded land-use data werealso spatially aggregated over each wetlandscape based on the area-weightedaveraging method (with weights of specific land-cover area in each grid cellrelative to total wetlandscape area). This provided a wetlandscape-specificdata time series of annual land use and land cover for each of the reclassified 10land-cover types. The final WetCID files comprised gridded time series dataon absolute grid cell area (in km2) covered by each land-cover type,time series data in percentage of grid cell area covered by each land-covertype, and aggregated absolute and percentage time series data for eachwetlandscape area.
Analogous to the hydroclimatic changes, period-specific changequantification can facilitate relatively simple and straightforward analysisof how different types of land-use changes between time periods correlatewith and may have driven associated wetlandscape changes (Destouni et al.,2013; Jaramillo and Destouni, 2015). Equation (1) was therefore also used tocalculate absolute change in the area of each land-cover type (km2)within each wetlandscape between Per1 (1981–1995) and Per2 (1996–2010). Inthe land-use case, and representannual average area covered by a land-cover type within each wetlandscapeduring Per1 and Per2, respectively. Furthermore, the corresponding change inrelative land-cover area (ChngRel in % of total wetlandscapearea) was calculated as
whereAreaC is the total wetlandscape (catchment) area (in km2) and and are the annual averageareas covered by each land-cover type in the wetlandscape during Per1 andPer2, respectively.
3.1 Site information surveys
Table 1 summarizes some general geographical, climate and wetland typeinformation provided by GWEN researchers in the survey information forms.Each site represents either an individual wetland or a wetlandscape (e.g., acatchment) including multiple wetlands. The country, main climate zone andwetland area relative to total wetlandscape (catchment) area are also givenfor each site in Table 1. Moreover, a summary of theavailability–accessibility of local data on the wetlands, hydrology,climate and land uses as well as the wetlandscape (catchment) area in eachof the 27 wetlandscapes is also shown in Fig. 2. The variables ofevapotranspiration and soil moisture were revealed as having large data gaps(red color in Fig. 2), indicating an overall need to use other datasources (e.g., gridded global data products) for quantifying these variablesand associated processes. Figure 2 also highlights the variability in dataavailability and open accessibility among the sites. For instance, no open-accessdata sources have been reported for the considered variables in the aridsubtropical sites 13, 16 and 17, whereas open-access data sources have beenreported for most variables in the cold Swedish sites 4 and 6 and theAmerican subtropical sites 15 and 19.
The synthesized survey dataset also contains information about differenttypes of wetland, hydroclimatic and/or land-use changes observed or perceivedto have occurred in the 27 investigated wetlandscapes (Fig. 3).Substantial changes are reported for most of these wetlandscapes, but a fewsites have no known changes (e.g., in the arid Moroccan site 17) or haveimportant knowledge gaps regarding changes (e.g., in the cold Swedish sites2 and 5, even though availability to at least some data is relatively goodthere). The information on local data availability–accessibility (Fig. 2)and observed or perceived change occurrence (Fig. 3) summarized andstructured in WetCID can guide further study directions and supportidentification of key needs for complementary new local data and/or use ofadditional large-scale (regional-global) gridded data. Furthermore, thewetlandscapes of WetCID are located in different regions of the world, withseven sites in northern Europe (sites 1–7), seven in the Amazon andCaribbean region (sites 20 and 23–27), four in North America (sites 10, 15,18 and 19), three in the Middle East (sites 12, 13 and 16), two in theMediterranean region (sites 11 and 14), two in Siberia (sites 8 and 9), andtwo more in other parts of the world (northern Africa and eastern Asia). Assuch, regional patterns and characteristics can be identified and regionalstrategies developed, e.g., to enhance availability of data and informationand determine further research needed to bridge region-specific knowledgegaps and decide on relevant management plans for each region's wetlandecosystems. Such regional characterizations and assessments can beinitialized with the current version of WetCID and further updated as moredata for already-included and possible additional regional wetlandscapesbecome available in future database versions.
Table 1General geographic, climate and wetland type information for the27 investigated wetlandscapes in WetCID. The data and information are basedon survey responses by researchers with active research (on various topics)at each wetlandscape site.
Figure 2Availability–accessibility (color-coded) of site-specific climate and land-use data for the 27 investigated wetlandscapes in WetCID andassociated wetlandscape area for each site (lower right diagram). The data availability–accessibility classification (color codes) is based on the surveyresponses by researchers with active research (on various topics) at each wetlandscape site.
Figure 3Knowledge status regarding observed or perceived changes occurring in the 27 investigated wetlandscapes in WetCID. The color-codedstatus classification is based on survey responses by researchers with active research (on various topics) at each wetlandscape site.
3.2 Hydroclimatic data
Data for long-term average temperature and precipitation conditions, andchanges in these between Per1 (1981–1995) and Per2 (1996–2010) at the 27wetlandscape sites, are presented in Fig. 4. The horizontal axis in thediagrams shows the wetlandscape site numbers in order of their latitude fromnorth to south, covering the latitude range from 70∘ N to25∘ S. The increase in average temperature and precipitation withdecreasing latitude (Fig. 4a–b) illustrates that the wetlandscapes alsocover a wide range of hydroclimate conditions, from low to high temperatureand precipitation values (see also Fig. 1). Temperature has increased overalmost all wetlandscapes and considerably more so in the more northern andcolder areas than in the warmer areas around and south of the Equator(Fig. 4a–b). In contrast, precipitation changes are relatively small,varying around zero, in the more northern, colder and drier areas,while precipitation has mostly increased in the warmer and also wetter areasaround and south of the Equator (Fig. 4c–e). Overall, the changes inmean annual temperature range from zero to+1 ∘C, while thechanges in precipitation range from−70 to+170 mm yr−1, with theIranian site (12) (Lake Urmia catchment) exhibiting the greatest increase intemperature (+1 ∘C) and the greatest relative decrease inprecipitation (−17 %).
Figure 4Overview of hydroclimate conditions and their changes in the 27wetlandscapes. Long-term average (1981–2010)(a) temperature (T) and(b) precipitation (Pr). Absolute change between Per1 (1981–1995) and Per2 (1996–2010)in(c) mean annual temperature and(d) mean annual precipitation.(e) Relative change in precipitation. The horizontal axis shows the numbering ofthe 27 wetlandscapes, sorted in order of their latitude from north to south.
Figures 5 and 6 exemplify gridded variability and change data fortemperature and precipitation over the Volga (no. 9) and the León–Atrato(no. 23) wetlandscapes. The data time series of wetlandscape-aggregatedannual average temperature and precipitation in these wetlandscapes (Fig. 5) exemplify such data prepared and included in WetCID for all 27wetlandscapes. These two wetlandscapes were chosen for data exemplificationbecause they represent different hydroclimatic conditions, with Volga beingcold and dry while León–Atrato is warm and wet (Fig. 5), as well ashaving different sizes, with Volga being the largest (1 360 000 km2) andLeón–Atrato (2344 km2) one of the smallest studied wetlandscapes.The data for these examples (Fig. 5) are consistent with correspondingdata implications across the different wetlandscapes over the world (Fig. 4) in indicating an overall positive (warmer–wetter) spatial correlationbetween long-term average temperature and precipitation. Temporally,however, the recent changes in these variables imply a negative correlation(towards warmer and mostly drier conditions) for the Volga wetlandscape(Fig. 6a and c) as for several other northern wetlandscapes in WetCID(Fig. 4). In contrast, a positive correlation (towards mostly warmer andwetter conditions) is implied by the recent temporal changes in theLeón–Atrato wetlandscape (Fig. 6b, d), as one of the most southernwetlandscapes in WetCID (Fig. 4). Such spatiotemporal sign shifts anddipole emergence in temperature–precipitation correlations have been notedin other recent studies of long-term variations and short-term changes ofhydroclimate over Europe (Charpentier Ljungqvist et al., 2019). WetCID canfacilitate further studies of these correlation conditions for and acrossthe different wetlandscapes around the world.
Figure 5Variability in wetlandscape-aggregated annual average temperatureand precipitation for the examples of the(a) Volga and(b) León–Atratowetlandscapes.
Figure 6Maps showing gridded absolute change in(a, b)temperature and(c, d) precipitation for the examples of the(a, c) Volga and(b, d) León–Atrato wetlandscapes. Absolute changevalues have been calculated by applying Eq. (1) to each grid cell within awetlandscape.
The data for the Volga and León–Atrato examples also emphasize thatwetlandscapes can have very different area extents (spatial scales), withpotentially important implications for the spatial resolution (Fig. 6) andrelated usefulness of data provided in WetCID. For example, the Volgawetlandscape includes 982 grid cells with complete or partial coverage inthe hydroclimate datasets, while the León–Atrato wetlandscape onlyincludes four of such grid cells. Most of the available global datasets fromclimate and earth system models have coarser spatial resolution than thesize of most individual wetlands. Thus, model data for individual wetlandsare subject to high uncertainty, whereas data aggregated over wholewetlandscapes have greater potential for accuracy (Bring et al., 2015),highlighting the need for considering the whole-wetlandscape scales inassessments of how wetland systems interact with hydroclimate and land-usechanges.
3.3 Land-use data
The aggregated and gridded land-use data in WetCID can also be used fordifferent types of whole-wetlandscape analyses. Figure 7 summarizes the datafor the long-term average relative area of each land-cover type (% of totalwetlandscape area), and associated absolute area changes (km2) andchanges in relative area coverage (% of total wetlandscape area),for different land-cover types across the 27 wetlandscapes. The data reveal,for example, the high percentage of forest area in wetlandscapes at highlatitudes and in the tropics, while relative cropland area increases towardsthe temperate regions (Fig. 7a). Figure 7 also summarizes thedifferent types of land-cover transformations, for example from “forest”into “cropland and pastureland” in the tropical Mekong wetlandscape (21),“pastureland” into “grassland” in the temperate Irish wetlandscape (7) andinto “cropland” in the borderline cold–dry Iranian wetlandscape of thedramatically shrinking Lake Urmia (12) (Khazaei et al., 2019), “shrubland”into “cropland” in the borderline temperate Iranian wetlandscape (13), and“cropland” into “shrubland” in the warm temperate Greek wetlandscape (14).
Figure 7(a) Long-term average relative area of each land-cover type(percentage of total wetlandscape area).(b) Absolute change in area ofeach land-cover type (km2).(c) Change in relative land-cover area(% in relation to total catchment area). The summarized andillustrated data are for the 27 wetlandscapes included in WetCID.
Figure 8Data time series for wetlandscape-aggregated annual average area(relative to total wetlandscape area, in %) for different land-covertypes in the(a) Volga and(b) León–Atrato wetlandscapes.
The data time series of different land-cover types and their changes between Per1(1981–1995) and Per2 (1996–2010) show, for example, forest and (decreasing)cropland, followed by pastureland and grassland, to be dominant in the largeVolga wetlandscape, while forest, pastureland and (decreasing) croplandareas dominate the small León–Atrato wetlandscape (Fig. 8). Griddedmaps of land-cover area changes in these wetlandscape examples (Figs. 9–10) again demonstrate large spatial-resolution differences, withpotentially important implications for the usefulness of land-use datasetsfor wetlandscapes of smaller scales. For example, in the most northernSwedish–Arctic wetlandscape (1), grassland is obtained as the second most dominantland-cover type after tundra (Fig. 7a), which is not normallyseen in this northern Arctic region.
Figure 9Gridded maps of absolute area changes (in km2) for(a) cropland,(b) pastureland,(c) urban,(d) forest,(e) shrubland,(f) desert,(g) grassland,(h) tundra and(i) water land-cover types between Per1(1981–1995) and Per2 (1996–2010) in the Volga wetlandscape example.
Figure 10Gridded maps of absolute area changes (in km2) for(a) cropland,(b) savanna,(c) forest,(d) shrubland,(e) grassland,(f) tundra,(g) pastureland and(h) urban land-cover types between Per1 (1981–1995) andPer2 (1996–2010) in the León–Atrato wetlandscape example.
The complete WetCID database includes five file categories(https://doi.org/10.1594/PANGAEA.907398; Ghajarnia et al., 2019).
Folder 1: survey results (summary documents A, B, C).These three summary documents (all in Excel) were created from responsesobtained in the main survey of GWEN researchers (see surveytemplate and structure in WetCID files). Summary document A contains summarizedsite-specific information on the wetlands, hydrology, climate and land usesin each of for the 27 wetlandscapes. Summary documents B and C contain localknowledge relating to the availability–accessibility (or lack) of land-useand hydroclimatic data, respectively, for each of the 27 wetlandscapes.
Folder 2: gridded land-use and hydroclimatic datasets (NetCDF database files).In WetCID, there is a separate NetCDF file for each wetlandscape thatcontains a complete set of gridded hydroclimate and land-use data timeseries for the closest rectangular window around the catchment polygon ofthe wetlandscape. The gridded hydroclimate datasets were created bysubsetting the CRU_TS4.02 original global datasets over thearea of each wetlandscape (catchment). The gridded land-use dataset for eachwetlandscape (catchment) was created by first reclassifying the land-covertypes and then subsetting the global gridded data. All these gridded datatime series are saved in separate NetCDF files for each wetlandscape, whichis an appropriate file type for storing gridded data. Each NetCDF filecontains 18 variables, including hydroclimate, land-cover and someauxiliary variables. Appendix B presents the table of general attributes (Table B1) and information and explanations of all 18 variables included in theNetCDF database files (Table B2). Sample MATLAB and R codes for reading andextracting data from the NetCDF files are also provided in Appendix C.
Folder 3: aggregated land-use and hydroclimate data (Excel databases).The time series of land-use and hydroclimatic data aggregated over eachwetlandscape (catchment) were created from the gridded datasets (NetCDFfiles) and stored as Excel files for each wetlandscape. The Excel file foreach wetlandscape contains three sheets: (1) annual time series of coveredarea by each land-cover type (in km2), (2) time series of annual relativearea (%) occupied by each land-cover type, and (3) time series of monthlytemperature (∘C) and precipitation (mm per month) data.
Folder 4: geographical dataset in a ZIP file (shapefiles).To perform any spatial analysis of the wetlandscapes, one needs to haveaccess to the shapefile and polygons of the wetlandscape (catchment) andwetlands within it. These shapefiles were provided by the GWEN researchersand can be downloaded from WetCID files.
Folder 5: summary tables of changes in hydroclimatic and land-use variables.Absolute and relative changes in all considered hydroclimate and land-usevariables between Per1 (1981–1995) and Per2 (1996–2010) were calculatedusing Eqs. (1), (2) and (3) for each wetlandscape. The results aresummarized in an Excel file with two sheets for each wetlandscape: (1) absolute changes in temperature, precipitation and land-cover area and (2) relative changes in precipitation and land-cover area. The data for land-cover changes are provided for all considered land-use variables.
The presented new database (WetCID) combines survey-based local informationand knowledge with gridded large-scale hydroclimate and land-use datasetsfor 27 wetlandscapes around the world. The gridded datasets contain 30-yeartime series of mean monthly precipitation and temperature along with annualaverage land uses and their changes over this time period for eachwetlandscape. WetCID can support site assessments; cross-regionalcomparisons; and scenario analyses of the roles and impacts of various land-use, hydroclimatic and wetland conditions and their changes onwhole-wetlandscape functions and associated ecosystem services. Theinformation on local data availability–accessibility and observed or perceivedchange occurrence summarized and structured in WetCID can guide furtherstudy directions and support identification of key needs for complementarynew local data and/or use of additional regional–global gridded datasets.
The gridded large-scale hydroclimatic and land-use data included in WetCIDhave been derived using open-access data sources and processed with open-sourcetools, while WetCID has been designed so that more data can readily be addedto it. The site-specific usefulness of different included data varies forwetlandscapes of different scales, but WetCID can be updated with small timeinvestment as new datasets become available or current datasets areexpanded or refined.
Table A1List of all different land-cover types included in the NOAA-HYDEdataset and their corresponding reclassified category in WetCID.
Table B1General attributes table for NetCDF database files of WetCID.
Table B2List and description of land-use and hydroclimate variablesincluded in the NetCDF database files of WetCID.
C1 MATLAB sample code
info = ncinfo('File_Name.nc'); % replaceFile_Name with the name of NetCDF file for each wetlandscape.This command gets the complete description for all the general attributes aswell as detailed information of all existing variables in the NetCDf file.
Var = ncread('File_Name.nc', 'Variable_Name'); % replace Variable_Name with the “Variable name”column in Table B2 for extracting different variable data from eachwetlandscape NetCDF file.
C2 R sample code
install.packages("ncdf4")
library(ncdf4)
ncf<-- nc_open("File_Name.nc") #replace File_Name with the name of NetCDF file for eachwetlandscape. This command opens the NetCDF file in RStudio environment.
names(ncf$var) # extracting the name of existing variables in theNetCDF file.
Var<-- ncvar_get(ncf, "Variable_Name") # replace Variable_Name with the “Variable name” columnin Table B2 for extracting different variable data from each wetlandscapeNetCDF file.
NG compiled the climate and land-use database, contributed to thecommunication with other co-authors for the wetlandscape data collection,and was mainly responsible for analyzing the data and writing the paper. GDconceived and led the study and the development of WetCID and analysisapproach, led the communication with other co-authors, and contributed tothe result analysis and writing of the paper. JT conceived the idea of thedata paper type; was mainly responsible for collecting and compiling the localsurvey information and its summary and analysis in thepaper; and contributed to communication with co-authors, the result analysisand the writing. ZK contributed to the communication with co-authors, thedatabase development, and the result analysis and writing. All otherco-authors contributed by providing local site information in the surveyforms and/or taking part in discussions for planning and outliningthe study.
The authors declare that they have no conflict of interest.
The Historical Land-Cover Change and Land-UseConversions Global Dataset used in this study was acquired from NOAA'sNational Climatic Data Center (http://www.ncdc.noaa.gov/, last access: 9 October 2018). Thetemperature and precipitation data were also retrieved from theCRU_TS4.02 global database (https://crudata.uea.ac.uk/cru/data/hrg/, last access: 9 May 2019).
This research has been supported by funding from the Swedish Research Council Formas (grant no. 2016-2045), the Russian RFBR project (grant nos. 17-29-05027 and 18-05-60219), the Russian Science Foundation project 14-17-00155 and the United States National Science Foundation (grant no. DEB-1237517, contribution number 958 from the Southeast Environmental Research Center at Florida International University).
This paper was edited by Alexander Gelfan and reviewed by two anonymous referees.
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- Abstract
- Introduction
- Methods
- Results
- Data availability
- Conclusions
- Appendix A: Summary of land-cover type parameters
- Appendix B: Description of parameters included in the NetCDF database filesof WetCID
- Appendix C: Sample codes to read NetCDF database files included in WetCID
- Author contributions
- Competing interests
- Acknowledgements
- Financial support
- Review statement
- References
- Abstract
- Introduction
- Methods
- Results
- Data availability
- Conclusions
- Appendix A: Summary of land-cover type parameters
- Appendix B: Description of parameters included in the NetCDF database filesof WetCID
- Appendix C: Sample codes to read NetCDF database files included in WetCID
- Author contributions
- Competing interests
- Acknowledgements
- Financial support
- Review statement
- References